Research ArticleEEG-Based Personality Prediction Using Fast Fourier Transformand DeepLSTM Model

Harshit Bhardwaj 1 Pradeep Tomar 1 Aditi Sakalle 1 and Wubshet Ibrahim 2

1CSE Department Gautam Buddha University Greater Noida India2Department of Mathematics Ambo University Ambo Ethiopia

Correspondence should be addressed to Wubshet Ibrahim wubshetibrahimamboueduet

Received 2 August 2021 Revised 30 August 2021 Accepted 3 September 2021 Published 22 September 2021

Academic Editor Gaurav Singal

Copyright copy 2021 Harshit Bhardwaj et al )is is an open access article distributed under the Creative Commons AttributionLicense which permits unrestricted use distribution and reproduction in any medium provided the original work isproperly cited

In this paper a deep long short term memory (DeepLSTM) network to classify personality traits using the electroencephalogram(EEG) signals is implemented For this research the MyersndashBriggs Type Indicator (MBTI) model for predicting personality isused )ere are four groups in MBTI and each group consists of two traits versus each other ie out of these two traits everyindividual will have one personality trait in them We have collected EEG data using a single NeuroSky MindWave Mobile 2 dryelectrode unit For data collection 40 Hindi and English video clips were included in a standard database All clips provoke variousemotions and data collection is focused on these emotions as the clips include targeted inductive scenes of personality Fiftyparticipants engaged in this research and willingly agreed to provide brain signals We compared the performance of our deeplearning DeepLSTMmodel with other state-of-the-art-based machine learning classifiers such as artificial neural network (ANN)K-nearest neighbors (KNN) LibSVM and hybrid genetic programming (HGP) )e analysis shows that for the 10-fold par-titioning method the DeepLSTMmodel surpasses the other state-of-the-art models and offers a maximum classification accuracyof 9694 )e proposed DeepLSTM model was also applied to the publicly available ASCERTAIN EEG dataset and showed animprovement over the state-of-the-art methods

1 Introduction

Personality has been developed from different theories butpersonality core is a function of individual behavioral dif-ferences and experiences affected by an individualrsquos devel-opment such as hisher emotions social relationships andlife experiences [1] Personality represents the action style ofa person in daily life )ere are many theories and per-sonality measurements but the personality trait measure-ments have become the most considerable acknowledgmentin the scientific community and play an irreplaceable role[2]

)ere are various ways in which personality predictioncan be made Personality can be identified by filling outquestionnaires also known as self-reported personality as-sessment )e five-factor personality test [3] and MBTIpersonality test [4 5] are its examples Personality prediction

can also be made using social media such as Twitter [6] andFacebook [7] data but that is not always so accurate becausethe data can be fake [8ndash10]

)e personality prediction using physiological signalshas recently received a lot of interest [11] )e physio-logical signal allows researchers to have a better under-standing of the participantrsquos reactions during theexperiment Recognizing personality from physiologicalsignals [12ndash14] is more accurate than digital footprints[15 16] because this approach achieves a higher classifi-cation accuracy

Among the physiological signals the electroencephalo-gram (EEG) signals have grown in prominence in recentyears and have achieved a higher classification accuracy[17 18] )e electrical activity produced by neurons in thebrain is recorded using EEG which have been widely utilisedto study functional changes in the brain [19 20]

HindawiComputational Intelligence and NeuroscienceVolume 2021 Article ID 6524858 10 pageshttpsdoiorg10115520216524858

EEG signals frequency varies from 05Hz to 100Hz andis grouped into five bands delta theta alpha beta andgamma all the bands have different frequencies [21 22] Aband of 05Hzndash50Hz is used for this paper

)e main contribution of this paper is as follows

(i) )e newly EEG dataset is created for personalityprediction using NeuroSky MindWave Mobile 2device

(ii) )is study proposed a DeepLSTM model for theprediction of personality traits

)e remaining paper is structured in the followingmanner Section 2 provides background details Section 3 isdevoted to the materials and methods used in this studySection 4 discusses the proposed personality frameworkSection 5 provides the experimental results Section 6 dis-cusses the comparison of the proposed DeepLSTM modelwith the other state-of-the-art methods Section 7 presentsthe conclusion

2 Background

)is section explains the FFT for extraction of the featuresand is discussed in detail next

21 Fast Fourier Transform )e first step in the successfulclassification [23 24] of personality traits is to extract im-portant EEG signal features )e popular methods for an-alyzing EEG data are decomposing signals into variousfrequency bands as shown in Figure 1 including delta (05to 4Hz) theta (4 to 8Hz) alpha (8 to 12Hz) beta (12 to30Hz) and gamma (30 to 100Hz) )e MindWave can usethe onboard chip )inkGear ASIC Module (TGAM1) withalgorithms that reduce the background noise and objectsFor a decomposing signal with fast Fourier transform (FFT)the TGAM1 chip has an algorithm )e value is provided tothe application program by the TGAM1 chip using thedevice Each second data are gathered and processed in thetemporal field to identify and correct as much as possible theartifacts and background noise without the practical usageof NeuroSkyrsquos proprietary algorithms of the original signal)e headset helps us to control meditation and attentionfeatures that their eSense technology measures

3 Materials and Methods

)is particular section gives details about the pool of par-ticipants details about the device used for experimentationthe details of the dataset used for experimentation and lastlydetails about the procedure of experimenting

31 Pool of Participants )is study consists of 55 partici-pants However five samples have been removed from thefinal assessment due to dware errors or inappropriate EEGsignal artifacts )erefore 50 representative samples of 18 to46 years of age (25 males and 25 females) participated in thestudy Forty participants are handed to the right ten arehanded to the left each with a natural vision Participants

were not allowed 24 hours before the experiment to taketobacco or caffeine

32DeviceDescription )eNeuroSky MindWave mobile 2devicersquos functionality is to capture brain signals as seen inFigure 2 )e brainwave reading EEG headset is simple tomonitor and is cheap It generates 12-bit (3ndash100 Hz) rawbrainwaves at a 512 Hz rate and generates EEG powerspectrums at various frequency and morphology bands Itis used for pairings with a static headset ID For capturingthe dataset eegId application is used which has in-buildFFT feature extraction technique and ten features areextracted

33 Proposed EEG Dataset Visual content is a reliablemeans of eliciting affect or emotion [25] in the literature Wecreated and consolidated a set of 40 movies and series clipsfor this analysis which served as elicitation materials for thedata collected from subjects)e content of these movie clipsincludes audio and video elements allowing students toparticipate in immersive experience English language andIndian language (Hindi) film samples with a length of about2 to 4 minutes were chosen for the process Each clip in theelicitation material includes content that evokes emotionsand personality traits and characters exhibiting a particularpersonality trait

All of the chosen movie clips are thought to generate andactivate the desired personality traitrsquos characteristic emo-tions Table 1 presents a selection of stimuli dataset clips usedto evoke a particular personality trait for EEG data acqui-sition )e order and selection of clips were randomized toensure effectiveness

34 Publicly Available Dataset )is research also uses thepublicly available EEG dataset of personality known asASCERTAIN dataset [26] )e ASCERTAIN dataset usesthe BFF model for personality prediction using EEGsignals which have been collected in laboratory settingsfrom the single-channel EEG device )e recorded in-formation includes frontal lobe activity level of facialactivation eye-blink rate and strength It contains 58

GAMMAActive Thought

ALPHARelaxed Reflective

THETADrowsy Meditative[

DELTASleepy Dreaming

BETAAlert Working

Figure 1 Brainwave frequency bands

2 Computational Intelligence and Neuroscience

participantsrsquo EEG recordings as data and 36 movie clipswere taken )ese clips are between 51 and 127 s long Alltopics were popular in English and the students wereregular film watchers from Hollywood )e film clips(nine clips per quadrant) are distributed uniformlythroughout the visual analog (VA) space For the re-cording of physiological signals different sensors wereused in the surveillance of the clips After watching theclip each participant was asked to mark the VA scale witha 7-point scale to represent his practical experience )epersonality test for the five large dimensions has also beenevaluated using a 5-dimensional questionnaire

4 Proposed Personality Prediction FrameworkUsing EEG Signals and DeepLSTM Model

Figure 3 includes the entire framework for personalityprediction using EEG signals and the DeepLSTMmodel)eproposed framework consists of two parts First is the datacollection for personality prediction and second is theDeepLSTMmodel for classification of personality traits andboth of these are described next

41 Data Collection for Personality Prediction Data collec-tion is the first step in the research process )is dataset wasobtained using an experimental protocol that is wellestablished and easy to follow )e dataset is created tosupport 50 volunteers (25 men and 25 women) who will beactively involved in the data collection process Since anindividualrsquos personality trait cannot be assessed solely bytheir current mood or state of mind the data will be collectedthree times over five days [27] )e participant was initiallyrelaxed in the data collection process and wore the NeuroSkyMindWave mobile 2 headset on their head Since there arefour groups in the MBTI personality traits each groupconsists of two traits in verses of each other )ere are eighttraits and for each trait one film clip is shown to theparticipant During the training time the proposed proce-dure is iterated eight times with one participant Before eachfilm clip the participants were given a 20-second startinghint to begin the test during which they viewed video clipsof a targeted personality trait Following that each partic-ipant signs a consent form which is then accompanied bykeeping a record of their general information such as nameage and gender at the initial levels for developing the

Figure 2 Single-channel NeuroSky MindWave mobile 2

Table 1 Sample of stimuli dataset clips to felicitate targeted personality traits for EEG data acquisition

Personality trait Film name Length(minutes) Clip content

Extrovert IBIZA 28min Harper an extrovert character exploring SpainIntrovert Jab We Met 322min An introvert guy boards a random train where he meets a bubbly girl

)inking Slumdog Millionaire 26min An uneducated nobody from slums answers critical questions of knowledge tobecome a millionaire

Feeling 12 Years a Slave 361min An incredible true story of Solomon Northup a free African-American abductedand sold into slavery fights not only to survive but to retain his dignity

Sensing URI )e SurgicalStrike 288min )e clip chronicles an event of the surgical strike lead by major for a covert

operation against suspected militants

Intuitive Confessions of aShopaholic 35min An abstract and imaginative character struggling with her enfeeble obsession

with shopping

Judging )e Devil WearsPrada 29min A famed fashion designer life story of being systematic schedule which inspires

both terror and a measure of awePerceiving 3 Idiots 30min An aspiring engineering student delivers a life lesson in a very innovative manner

Computational Intelligence and Neuroscience 3

dataset Single-channel EEG adjustable headband was usedto monitor the EEG signals

After viewing a film clip of one trait the participants hadto fill the self-evaluation form with options ldquoagreerdquo ldquoneu-tralrdquo or ldquodisagreerdquo and have seven questionnaires for eachpersonality trait )ese questionnaires are constructed bytargeting the characteristics of personality traits )esequestionnaires must be answered based on the participantsrsquoreal feelings instead of their typical emotions or generalattitude which may differ from person to person Because ofthat the answer to those questionnaires may differ In eachclip a 1-minute buffer is for neutral clip to neutralize theparticipantsrsquo elicited personality traits After all of thequestions for each of the four grouped personality traits havebeen answered the questionnaire (which contains sevenquestions) is evaluated for each participantrsquos traits )elabeling of the EEG signal depends on the output of thequestionnaires given by the participant )e final output isevaluated by the following procedure Let us suppose that theparticipant has watched the film clip targeting the charac-teristic of the extraversion trait After watching the film clipthe participant answered the questionnaires based on theextraversion trait Suppose the participant selects for theldquoagreerdquo option in the questionnaire In that case we can raiseit by value 1 If for the extraversion questionnaire theparticipant chooses the option ldquodisagreerdquo we raise thecounter of the introversion trait (versus trait of extraversion)by one If the participant opts for the neutral option weneither increase nor decrease the counter for any trait Sincethere are seven extraversion trait questionnaires the EEGsignal labeling depends on the participantrsquos output andthree labeling possibilities exist

(i) )e EEG signal is labeled as extraversion if thenumber of ldquoagreerdquo options is more selected thanldquodisagreerdquo

(ii) )e EEG signal is labeled as introversion if thenumber of ldquodisagreerdquo options is more selected thanldquoagreerdquo

(iii) )e EEG signal is discarded if the number of ldquoagreerdquoand ldquodisagreerdquo options are equal in number

Similarly for introversion trait-based questionnaires thecounter for introversion trait is incremented if the partici-pant chooses the ldquoagreerdquo option If the participant choosesthe ldquodisagreerdquo option the counter for extraversion isincremented If the participant opts for the neutral optionwe do not increase or diminish the counter for that

questionnaire of both the traits )e labeling of the EEGsignal is done by following the above procedure Similarlythe remaining personality traits marking is done and theirrelated EEG signals are labeled )e same experimentalprocedure is repeated after three days for collecting the dataand removing bias

At the end of each traitrsquos evaluation process the datasetrsquosmaximum counter value is labeled To label the EEG signalsthis marking scheme is taken as the reference )e studyrsquostesting data were obtained using just four video clips tar-geting one personality trait from each group

)e experiment will be performed using four machinelearning algorithms ANN KNN LibSVM and HGP in-cluding our proposed DeepLSTM classifier )e survey andreview of results for the recorded EEG signal dataset usingthe described machine learning algorithms will providevaluable material for a similar study of personality types)ese findings show that personality inference from EEGsignals outperforms state-of-the-art clear behavioral indi-cators in classification accuracy

42 Proposed DeepLSTM Model Various algorithms forlearning machines are used for the recognition and de-scription of personality characteristics in literature )eDeepLSTM model for personality traits classification withthe use of EEG signals is used in this work

Figure 4 includes the architectures of the DeepLSTM cellnetwork used for classifying the personality traits by usingEEG signals in this analysis )e DeepLSTM network hasbeen established on the backend in Python 36 Keras 209 onTensorFlow 140

In DeepLSTM architecture there are 3 LSTM layerswith 512 memory units in the first layer 256 memory unitsin the second layer and 128 memory units in the thirdlayer In all proposed architectures the dropout layer isalso used and the probability value is 02 In the modelbetween existing layers the dropout layer is applied toprevious layer outputs which are fed to the layer asshown in Figure 4 A layerrsquos outputs are arbitrarily sub-sampled under dropout layer )e memorization capa-bility of the DeepLSTM model is due to the dropoutregularization [28] Furthermore the model is trainedfaster with the 02 dropouts overfitting is reduced and theproposed DeepLSTM model performs better in terms ofprediction )e ldquotanhrdquo function is used as an activationfunction and generates the output of 64 units

StimuliEEG Data

AcquisitionFeature

ExtractionDeepLSTM

Model

DataClassification

PersonalityPrediction

f1f2f3f10

EEG Signals AreGenerated While

Watching

E vs IS vs IT vs FJ vs P

Figure 3 Personality prediction framework using EEG signals and DeepLSTM model

4 Computational Intelligence and Neuroscience

tanh(x) 2

1 + eminus2x

minus 1 (1)

ldquoSoftmaxrdquo is used as an activation function in the lastlayer and 4 outputs are generated representing four per-sonality classes )e key benefit of using the softmax as anactivation function is the range of output probabilitieswhich will be between 0 and 1 It returns each classrsquosprobabilities with the target class having the highestprobability

Softmax xi( 1113857 exp xi( 1113857

1113936jexp xj1113872 1113873 (2)

LSTM cells and dropout layers are utilised to discoverthe role of EEG signals )e overfitting of these systems wasminimized by restricting unit coadapting in the dropoutlayer of our DeepLSTM architectures )e dense layer theloss function for these network architectures is categoricalcross-entropy and the batch size is 40 )e adaptive momentestimation optimizer (Adam) is used for a learning rate of0001 )e normalization is applied to the dataset inputfeatures with the MinMaxScaler function after loading thedataset )is function normalizes each feature because ofwhich each feature contributes in a maintained manner Itdecreases the internal covariate transition resulting in achange in network activation distribution due to shifts innetwork parameters during training )e normalization ofthe proposed network enhances training reducing thechange in the internal covariance It also helped improve theoptimization phase by stopping weights from burstingaround the entire site by limiting them to a specific set Anundesired advantage of normalization is that it often allowsthe mechanism to regularize somewhat In the parameterspecified by Table 2 the proposed DeepLSTM network isinitialized [29] We test the output of our proposedDeepLSTMmodel which classifies EEG signals as an outputvalue into five personality groups for 500 epochs with a

batch size of 40 )e DeepLSTM model was evaluated usingthe suggested EEG dataset as well as the publicly availableASCERTAIN EEG dataset

)e proposed architectures and parameters were chosenbased on our own experiments with nearby architectures (interms of layers and nodes) In terms of accuracy the pro-posed DeepLSTM architecture outperforms their nearbyarchitecture

5 Experimental Results

)e results of the DeepLSTM model for classifying the EEGsignals and to check our systemrsquos efficacy are presented next)e computer environment is composed of 34GHz devotedto the 32GB RAM-based Python (36) to incorporateDeepLSTM cell architecture and other states of the art ieANN KNN LibSVM and HGP)e parameter values of theANN KNN LibSVM and HGP are the same as in [19 30]respectively )e parameter values taken for the imple-mentation of the DeepLSTM model are given in Table 2

)e dataset is typically split into two distinct sets ietraining sets and test sets A general review of our method iscarried out in this research of personality trait classificationusing EEG signals We have separated the dataset intodifferent training and testing partitions to equate them withexisting literature)e performance assessment is conductedusing a 50ndash50 60ndash40 70ndash30 and 10-fold partition scheme

512 Memory Unit

256 Memory Unit

128 Memory Unit

64 Memory Unit

Input Features

LSTM Layer 1

LSTM Layer 2

LSTM Layer 3

Tanh

SoMax4 Output

DROPOUT LAYER

DROPOUT LAYER

DROPOUT LAYER

DeepLSTM

F1 F2 F3 F10

Figure 4 DeepLSTM cells network architecture

Table 2 DeepLSTM model formation parameter values

Parameter ValueOptimizer AdamRate of learning 0001Rate of dropout 02Loss function Categorical cross-entropyMetrics used AccuracySize of batch 40

Computational Intelligence and Neuroscience 5

In 50ndash50 60ndash40 and 70ndash30 training-testing partition50 60 and 70 respectively data is used for trainingand 50 40 and 30 respectively of the data is used fortesting )e complete dataset is partitioned into approxi-mately ten equal size blocks in a 10-fold cross-validationscheme 90 of the dataset ie nine blocks becomes ourtraining data and 10 of the dataset ie one block becomesour testing data)is process is repeated ten times with eachtime a different data block being used for testing Also ourproposed modelrsquos sensitivity precision and specificity valuefor the 50ndash50 60ndash40 70ndash30 and 10-fold partition schemesare calculated

51 DeepLSTM Architecture Evaluation )is study uses adeep learning algorithm to distinguish personality traitsfrom EEG signals In practice the DeepLSTM model out-performs traditional machine learning algorithms because ithas the capability of remembering the long-term depen-dence of sequential data in time increasing the likelihood ofcorrectness in a short period of time [31]

Table 3 represents the classification accuracy comparisonfor personality prediction DeepLSTM model on the AS-CERTAIN and the proposed EEG datasets For the AS-CERTAIN and the proposed EEG datasets the proposedDeepLSTM model maximum average and minimumclassification accuracy for 50ndash50 60ndash40 70ndash30 and 10-foldcross-validation partition scheme is calculated

)e maximum classification accuracy of the proposedDeepLSTM model for 50ndash50 60ndash40 70ndash30 and 10-foldcross-validation partition scheme on the ASCERTAIN EEGdataset is 8248 8814 9286 and 9532 respectively

)e maximum classification accuracy of the proposedDeepLSTM model for 50ndash50 60ndash40 70ndash30 and 10-foldcross-validation partition scheme on the proposed EEGdataset is 8456 9152 9482 and 9694 respectivelyFrom the results it can be seen that the DeepLSTM modelperforms better in terms of performance on the ASCER-TAIN and the proposed EEG datasets and the classificationaccuracy of the DeepLSTMmodel is higher on our proposedEEG dataset than the ASCERTAIN dataset

6 Discussion

)is section discusses how the proposed deep learning-basedDeepLSTM model works compared to conventional ma-chine learning algorithms

A comparison with standard conventional classificationalgorithms is carried out using the same collection of fea-tures as used in DeepLSTM-based methodology to show theadvantages of incorporating deep learning into the classi-fication of personality traits

)e KNN ANN LibSVM and HGP are the other state-of-the-art approaches used for comparison )e classifica-tion accuracy comparison of personality traits is containedin Table 4 It contains the maximum average and minimumaccuracy for the 50ndash50 60ndash40 70ndash30 and 10-fold partitionschemes )e parameters and settings for these variableshave all been implemented using the same technique toensure that the findings and comparisons offered are un-ambiguous and consistent

)e proposed deep learning approach has a greaterimpact than traditional machine learning algorithms )eDeepLSTM classification improved dramatically in clas-sification accuracy as per the results Besides the rise inclassification accuracy the DeepLSTM classifier can alsoretain specificity greater than 9286 on the ASCERTAINdataset and 9384 on the proposed EEG dataset resultingin very low false prediction rates )e sensitivity value ofthe DeepLSTM model for the ASCERTAIN dataset is9472 and the proposed EEG dataset is 9586 and ishigh in the other state-of-the-art methods which showsthat the DeepLSTM model correctly classifies the minorityclass samples )e precision value of the DeepLSTMmodelfor the ASCERTAIN dataset is 9348 and the proposedEEG dataset is 9444 and is high in the other state-of-the-art methods )e F1 score value of the DeepLSTM modelfor the ASCERTAIN dataset is 9368 and the proposedEEG dataset is 9496 and is high in the other state-of-the-art methods Table 5 shows the relation of sensitivityprecision specificity and F1 score values of DeepLSTM for50ndash50 60ndash40 70ndash30 and 10-fold data partitioningscheme

Table 6 shows the statistical result disparity is illustratedby the two-tailed MannndashWhitney test [32] )e Man-nndashWhitney test is used to compute the p value relation inclassification accuracy )e outcomes do not change sig-nificantly if the p value is greater than 005 and it is highly

Table 3 Classification accuracy comparison for personality pre-diction of DeepLSTM model on the ASCERTAIN and proposedEEG datasets

Dataset Method Validationtechnique

AccuracyMax Avg Min

ASCERTAIN DeepLSTMclassifier

50ndash50 8248 8036 774260ndash40 8814 8563 814670ndash30 9286 8968 864610-fold 9532 9416 9198

Proposeddataset

DeepLSTMclassifier

50ndash50 8456 8244 796260ndash40 9152 8762 848670ndash30 9482 9168 887210-fold 9694 9588 9394

Optimal values are represented in bold

6 Computational Intelligence and Neuroscience

Table 5 Comparison of sensitivity precision and specificity of DeepLSTM model for various partition schemes

Dataset Validation techniqueSensitivity () Precision () Specificity () F1 score ()Meanplusmn Std Meanplusmn Std Meanplusmn Std Meanplusmn Std

ASCERTAIN

50ndash50 8164 plusmn308 8053 plusmn312 7842 plusmn284 8056 plusmn23660ndash40 8776 plusmn314 8682 plusmn324 8594 plusmn268 8644 plusmn24470ndash30 9149 plusmn316 9056 plusmn342 8967 plusmn236 9040 plusmn34210-fold 9472 plusmn 316 9348 plusmn 312 9286 plusmn 298 9368 plusmn 284

Proposed dataset

50ndash50 8346 plusmn314 8275 plusmn315 8114 plusmn324 8246 plusmn31860ndash40 9074 plusmn324 8925 plusmn316 8894 plusmn318 8946 plusmn33270ndash30 9354 plusmn314 9285 plusmn325 9194 plusmn342 9228 plusmn32410-fold 9586 plusmn 318 9444 plusmn 314 9384 plusmn 312 9496 plusmn 316

Optimal values are represented in bold

Table 4 Classification accuracy comparison for personality prediction of DeepLSTM model with other state-of-the-art algorithms on theASCERTAIN and the proposed EEG dataset

Dataset Method Validation techniqueAccuracy

Max Avg Min

ASCERTAIN

ANN 50ndash50 7084 6724 6448KNN 50ndash50 6764 6426 6223

LIBSVM 50ndash50 7728 7384 7186HGP 50ndash50 7852 7538 7268

DeepLSTM classifier 50ndash50 8248 8036 7742

ASCERTAIN

ANN 60ndash40 7434 6986 6774KNN 60ndash40 7038 6816 6568

LIBSVM 60ndash40 7986 7728 7546HGP 60ndash40 8127 7873 7608

DeepLSTM classifier 60ndash40 8814 8563 8146

ASCERTAIN

ANN 70ndash30 7518 7316 6994KNN 70ndash30 7282 7084 6862

LIBSVM 70ndash30 8326 8162 7986HGP 70ndash30 8664 8338 8074

DeepLSTM classifier 70ndash30 9286 8968 8646

ASCERTAIN

ANN 10-fold 7882 7464 7246KNN 10-fold 7436 7237 7025

LIBSVM 10-fold 8482 8242 8028HGP 10-fold 8612 8386 8184

DeepLSTM classifier 10-fold 9532 9416 9198

Proposed dataset

ANN 50ndash50 7284 6936 6648KNN 50ndash50 6932 6692 6386

LIBSVM 50ndash50 7974 7664 7386HGP 50ndash50 8036 7783 7489

DeepLSTM classifier 50ndash50 8456 8244 7962

Proposed dataset

ANN 60ndash40 7616 7328 7012KNN 60ndash40 7264 7062 6854

LIBSVM 60ndash40 8126 7958 7782HGP 60ndash40 8328 8094 7863

DeepLSTM classifier 60ndash40 9152 8762 8486

proposed dataset

ANN 70ndash30 7862 7394 7128KNN 70ndash30 7468 7236 7082

LIBSVM 70ndash30 8564 8370 8158HGP 70ndash30 8769 8474 8284

DeepLSTM classifier 70ndash30 9482 9168 8872

Proposed dataset

ANN 10-fold 8024 7654 7498KNN 10-fold 7682 7464 7228

LIBSVM 10-fold 8804 8516 8326HGP 10-fold 9032 8693 8478

DeepLSTM classifier 10-fold 9694 9588 9394Optimal values are represented in bold

Computational Intelligence and Neuroscience 7

Tabl

e6

pvaluecomparisonforDeepL

STM

usingMannndash

Whitney

test

Training

-testin

gANN

KNN

LibSVM

HGP

Partition

pvalue

Sign

ificance

p-value

Sign

ificance

pvalue

Sign

ificance

pvalue

Sign

ificance

DeepL

STM

50ndash5

02681x

10minus11

Highly

Sign

ificant

1450x

10minus11

Highly

Sign

ificant

19543

10minus11

Highly

Sign

ificant

18563

10minus11

Highly

Sign

ificant

60ndash4

02634x

10minus11

Highly

Sign

ificant

1350x

10minus11

Highly

Sign

ificant

18703

10minus11

Highly

Sign

ificant

18363

10minus11

Highly

Sign

ificant

70ndash3

02576x

10minus11

Highly

Sign

ificant

1270x

10minus11

Highly

Sign

ificant

18423

10minus11

Highly

Sign

ificant

17946

10minus11

Highly

Sign

ificant

10-fold

2486x

10minus11

Highly

Sign

ificant

1235x

10minus11

Highly

Sign

ificant

17856

10minus11

Highly

Sign

ificant

17637

10minus11

Highly

Sign

ificant

Optim

alvalues

arerepresentedin

bold

8 Computational Intelligence and Neuroscience

significant if the p value is less than 0001 It is evident fromthe interventions in Table 6 that the solution provided by ourproposed DeepLSTM model is statistically different fromANN KNN LibSVM andHGP for the 50ndash50 60ndash40 70ndash30and 10-fold data partitioning scheme When the p values arecontrasted with DeepLSTM for these classifiers there is asignificant variation in outcomes )e evaluation resultssuggest that the proposed DeepLSTM-based deep learningmodel for classifying personality traits provides accurateclassification results

7 Conclusion

During this study we propose EEG signals-based personalityprediction system using DeepLSTM-based deep learningmodel

A new EEG dataset was also created using 40 film clips ofHindi and English languages )e proposed DeepLSTMmodel was also applied to the publicly available EEG datasetknown as ASCERTAIN Multiple experiments have beencarried out to validate our results which are helpful tocompare our DeepLSTMmodel with existing methods Fiftyparticipants were involved and saw a few movie clips tar-geting eight different personality traits )is method usesNeuroSky MindWave mobile 2 to capture brain signalsBetter results of sensitivity precision and specificity indicatethat our approach beats the current literature )e classi-fication accuracy of the proposed DeepLSTM model on ourproposed EEG dataset is 9694 for the 10-fold partitionscheme and outperforms the results of the DeepLSTMmodel on the ASCERTAIN dataset having classificationaccuracy of 9532

We are currently using a single-channel device and inthe future we will extend it to multichannel devices

Data Availability

)e data are available on request from the correspondingauthor

Conflicts of Interest

)e authors declare that they have no conflicts of interest

References

[1] D P McAdams and B D Olson ldquoPersonality developmentcontinuity and change over the life courserdquo Annual Review ofPsychology vol 61 no 1 pp 517ndash542 2010

[2] O P John S E Hampson and L R Goldberg ldquo)e basic levelin personality-trait hierarchies studies of trait use and ac-cessibility in different contextsrdquo Journal of Personality andSocial Psychology vol 60 no 3 pp 348ndash361 1991

[3] F M Cheung and K Leung ldquoIndigenous personality mea-suresrdquo Journal of Cross-Cultural Psychology vol 29 no 1pp 233ndash248 1998

[4] R Harrington and D A Loffredo ldquoMbti personality type andother factors that relate to preference for online versus face-to-face instructionrdquo e Internet and Higher Educationvol 13 no 1-2 pp 89ndash95 2010

[5] R R McCrae and O P John ldquoAn introduction to the five-factor model and its applicationsrdquo Journal of Personalityvol 60 no 2 pp 175ndash215 1992

[6] S K Bhatti A Muneer M I Lali M Gull and S M U DinldquoPersonality analysis of the USA public using twitter profilepicturesrdquo in Proceedings of the 2017 International Conferenceon Information and Communication Technologies (ICICT)pp 165ndash172 Karachi Pakistan December 2017

[7] Y Gvili O Kol and S Levy ldquo)e value (s) of information onsocial network sites the role of user personality traitsrdquo Eu-ropean Review of Applied Psychology vol 70 Article ID100511 2019

[8] A Eftekhar C Fullwood and N Morris ldquoCapturing per-sonality from facebook photos and photo-related activitieshow much exposure do you needrdquo Computers in HumanBehavior vol 37 pp 162ndash170 2014

[9] U Oberst V Renau A Chamarro and X Carbonell ldquoGenderstereotypes in facebook profiles are women more femaleonlinerdquo Computers in Human Behavior vol 60 pp 559ndash5642016

[10] Y-C J Wu W-H Chang and C-H Yuan ldquoDo Facebookprofile pictures reflect userrsquos personalityrdquo Computers inHuman Behavior vol 51 pp 880ndash889 2015

[11] G Stemmler and J Wacker ldquoPersonality emotion and in-dividual differences in physiological responsesrdquo BiologicalPsychology vol 84 no 3 pp 541ndash551 2010

[12] A Baumert M Schmitt and M Perugini ldquoTowards an ex-planatory personality psychology integrating personalitystructure personality process and personality developmentrdquoPersonality and Individual Differences vol 147 pp 18ndash272019

[13] S Kreitler ldquoTowards a consensual model in personalitypsychologyrdquo Personality and Individual Differences vol 147pp 156ndash165 2019

[14] C Montag and J D Elhai ldquoA new agenda for personalitypsychology in the digital agerdquo Personality and IndividualDifferences vol 147 pp 128ndash134 2019

[15] R Akhtar D Winsborough U Ort A Johnson andT Chamorro-Premuzic ldquoDetecting the dark side of per-sonality using social media status updatesrdquo Personality andIndividual Differences vol 132 pp 90ndash97 2018

[16] A C E S Lima and L N De Castro ldquoA multi-label semi-supervised classification approach applied to personalityprediction in social mediardquo Neural Networks vol 58pp 122ndash130 2014

[17] A Bhardwaj A Tiwari M V Varma and M R KrishnaldquoClassification of eeg signals using a novel genetic pro-gramming approachrdquo in Proceedings of the CompanionPublication of the 2014 Annual Conference on Genetic andEvolutionary Computation pp 1297ndash1304 Ireland 2014

[18] H Bhardwaj A Sakalle A Bhardwaj and A TiwarildquoClassification of electroencephalogram signal for the de-tection of epilepsy using innovative genetic programmingrdquoExpert Systems vol 36 no 1 Article ID e12338 2019

[19] A Bhardwaj A Tiwari M V Varma and M R Krishna ldquoAnanalysis of integration of hill climbing in crossover andmutation operation for eeg signal classificationrdquo in Pro-ceedings of the 2015 Annual Conference on Genetic andEvolutionary Computation pp 209ndash216 ACM YangonMyanmar August 2015

[20] T Takahashi T Murata T Hamada et al ldquoChanges in eegand autonomic nervous activity during meditation and theirassociation with personality traitsrdquo International Journal ofPsychophysiology vol 55 no 2 pp 199ndash207 2005

Computational Intelligence and Neuroscience 9

[21] H Bhardwaj P Tomar A Sakalle and A Bhardwaj ldquoClas-sification of extraversion and introversion personality traitusing electroencephalogram signalsrdquo Communications inComputer and Information Science vol 1434 pp 31ndash39 2021

[22] A Sakalle P Tomar H Bhardwaj and A BhardwajldquoEmotion recognition using portable eeg devicerdquo in Com-munications in Computer and Information Science pp 17ndash30Springer International Publishing Berlin Germany 2021

[23] A Kumar N Sinha and A Bhardwaj ldquoA novel fitnessfunction in genetic programming for medical data classifi-cationrdquo Journal of Biomedical Informatics vol 112 Article ID103623 2020

[24] A Purohit A Bhardwaj A Tiwari and N S ChoudharildquoRemoving code bloating in crossover operation in geneticprogrammingrdquo in Proceedings of the 2011 InternationalConference on Recent Trends in Information Technology(ICRTIT) pp 1126ndash1130 Chennai 2011

[25] S Fayolle S Droit-Volet and S Gil ldquoEmotion and timeperception effects of film-induced moodrdquo Procedia - Socialand Behavioral Sciences vol 126 pp 251-252 2014

[26] R Subramanian J Wache M K Abadi R L VieriuS Winkler and N Sebe ldquoAscertain emotion and personalityrecognition using commercial sensorsrdquo IEEE Transactions onAffective Computing vol 9 pp 147ndash160 2016

[27] A Caspi B W Roberts and R L Shiner ldquoPersonality de-velopment stability and changerdquo Annual Review of Psy-chology vol 56 no 1 pp 453ndash484 2005

[28] W Zaremba I Sutskever and O Vinyals ldquoRecurrent neuralnetwork regularizationrdquo 2014 httpsarxivorgabs14092329

[29] A Sakalle P Tomar H Bhardwaj D Acharya andA Bhardwaj ldquoA lstm based deep learning network for rec-ognizing emotions using wireless brainwave driven systemrdquoExpert Systems with Applications vol 173 Article ID 1145162021

[30] A M Bhatti M Majid S M Anwar and B Khan ldquoHumanemotion recognition and analysis in response to audio musicusing brain signalsrdquo Computers in Human Behavior vol 65pp 267ndash275 2016

[31] Y Bengio P Simard and P Frasconi ldquoLearning long-termdependencies with gradient descent is difficultrdquo IEEETransactions on Neural Networks vol 5 no 2 pp 157ndash1661994

[32] H B Mann and D R Whitney ldquoOn a test of whether one oftwo random variables is stochastically larger than the otherrdquoeAnnals of Mathematical Statistics vol 18 no 1 pp 50ndash601947

10 Computational Intelligence and Neuroscience

participantsrsquo EEG recordings as data and 36 movie clipswere taken )ese clips are between 51 and 127 s long Alltopics were popular in English and the students wereregular film watchers from Hollywood )e film clips(nine clips per quadrant) are distributed uniformlythroughout the visual analog (VA) space For the re-cording of physiological signals different sensors wereused in the surveillance of the clips After watching theclip each participant was asked to mark the VA scale witha 7-point scale to represent his practical experience )epersonality test for the five large dimensions has also beenevaluated using a 5-dimensional questionnaire

4 Proposed Personality Prediction FrameworkUsing EEG Signals and DeepLSTM Model

Figure 3 includes the entire framework for personalityprediction using EEG signals and the DeepLSTMmodel)eproposed framework consists of two parts First is the datacollection for personality prediction and second is theDeepLSTMmodel for classification of personality traits andboth of these are described next

41 Data Collection for Personality Prediction Data collec-tion is the first step in the research process )is dataset wasobtained using an experimental protocol that is wellestablished and easy to follow )e dataset is created tosupport 50 volunteers (25 men and 25 women) who will beactively involved in the data collection process Since anindividualrsquos personality trait cannot be assessed solely bytheir current mood or state of mind the data will be collectedthree times over five days [27] )e participant was initiallyrelaxed in the data collection process and wore the NeuroSkyMindWave mobile 2 headset on their head Since there arefour groups in the MBTI personality traits each groupconsists of two traits in verses of each other )ere are eighttraits and for each trait one film clip is shown to theparticipant During the training time the proposed proce-dure is iterated eight times with one participant Before eachfilm clip the participants were given a 20-second startinghint to begin the test during which they viewed video clipsof a targeted personality trait Following that each partic-ipant signs a consent form which is then accompanied bykeeping a record of their general information such as nameage and gender at the initial levels for developing the

Figure 2 Single-channel NeuroSky MindWave mobile 2

Table 1 Sample of stimuli dataset clips to felicitate targeted personality traits for EEG data acquisition

Personality trait Film name Length(minutes) Clip content

Extrovert IBIZA 28min Harper an extrovert character exploring SpainIntrovert Jab We Met 322min An introvert guy boards a random train where he meets a bubbly girl

)inking Slumdog Millionaire 26min An uneducated nobody from slums answers critical questions of knowledge tobecome a millionaire

Feeling 12 Years a Slave 361min An incredible true story of Solomon Northup a free African-American abductedand sold into slavery fights not only to survive but to retain his dignity

Sensing URI )e SurgicalStrike 288min )e clip chronicles an event of the surgical strike lead by major for a covert

operation against suspected militants

Intuitive Confessions of aShopaholic 35min An abstract and imaginative character struggling with her enfeeble obsession

with shopping

Judging )e Devil WearsPrada 29min A famed fashion designer life story of being systematic schedule which inspires

both terror and a measure of awePerceiving 3 Idiots 30min An aspiring engineering student delivers a life lesson in a very innovative manner

Computational Intelligence and Neuroscience 3

dataset Single-channel EEG adjustable headband was usedto monitor the EEG signals

After viewing a film clip of one trait the participants hadto fill the self-evaluation form with options ldquoagreerdquo ldquoneu-tralrdquo or ldquodisagreerdquo and have seven questionnaires for eachpersonality trait )ese questionnaires are constructed bytargeting the characteristics of personality traits )esequestionnaires must be answered based on the participantsrsquoreal feelings instead of their typical emotions or generalattitude which may differ from person to person Because ofthat the answer to those questionnaires may differ In eachclip a 1-minute buffer is for neutral clip to neutralize theparticipantsrsquo elicited personality traits After all of thequestions for each of the four grouped personality traits havebeen answered the questionnaire (which contains sevenquestions) is evaluated for each participantrsquos traits )elabeling of the EEG signal depends on the output of thequestionnaires given by the participant )e final output isevaluated by the following procedure Let us suppose that theparticipant has watched the film clip targeting the charac-teristic of the extraversion trait After watching the film clipthe participant answered the questionnaires based on theextraversion trait Suppose the participant selects for theldquoagreerdquo option in the questionnaire In that case we can raiseit by value 1 If for the extraversion questionnaire theparticipant chooses the option ldquodisagreerdquo we raise thecounter of the introversion trait (versus trait of extraversion)by one If the participant opts for the neutral option weneither increase nor decrease the counter for any trait Sincethere are seven extraversion trait questionnaires the EEGsignal labeling depends on the participantrsquos output andthree labeling possibilities exist

(i) )e EEG signal is labeled as extraversion if thenumber of ldquoagreerdquo options is more selected thanldquodisagreerdquo

(ii) )e EEG signal is labeled as introversion if thenumber of ldquodisagreerdquo options is more selected thanldquoagreerdquo

(iii) )e EEG signal is discarded if the number of ldquoagreerdquoand ldquodisagreerdquo options are equal in number

Similarly for introversion trait-based questionnaires thecounter for introversion trait is incremented if the partici-pant chooses the ldquoagreerdquo option If the participant choosesthe ldquodisagreerdquo option the counter for extraversion isincremented If the participant opts for the neutral optionwe do not increase or diminish the counter for that

questionnaire of both the traits )e labeling of the EEGsignal is done by following the above procedure Similarlythe remaining personality traits marking is done and theirrelated EEG signals are labeled )e same experimentalprocedure is repeated after three days for collecting the dataand removing bias

At the end of each traitrsquos evaluation process the datasetrsquosmaximum counter value is labeled To label the EEG signalsthis marking scheme is taken as the reference )e studyrsquostesting data were obtained using just four video clips tar-geting one personality trait from each group

)e experiment will be performed using four machinelearning algorithms ANN KNN LibSVM and HGP in-cluding our proposed DeepLSTM classifier )e survey andreview of results for the recorded EEG signal dataset usingthe described machine learning algorithms will providevaluable material for a similar study of personality types)ese findings show that personality inference from EEGsignals outperforms state-of-the-art clear behavioral indi-cators in classification accuracy

42 Proposed DeepLSTM Model Various algorithms forlearning machines are used for the recognition and de-scription of personality characteristics in literature )eDeepLSTM model for personality traits classification withthe use of EEG signals is used in this work

Figure 4 includes the architectures of the DeepLSTM cellnetwork used for classifying the personality traits by usingEEG signals in this analysis )e DeepLSTM network hasbeen established on the backend in Python 36 Keras 209 onTensorFlow 140

In DeepLSTM architecture there are 3 LSTM layerswith 512 memory units in the first layer 256 memory unitsin the second layer and 128 memory units in the thirdlayer In all proposed architectures the dropout layer isalso used and the probability value is 02 In the modelbetween existing layers the dropout layer is applied toprevious layer outputs which are fed to the layer asshown in Figure 4 A layerrsquos outputs are arbitrarily sub-sampled under dropout layer )e memorization capa-bility of the DeepLSTM model is due to the dropoutregularization [28] Furthermore the model is trainedfaster with the 02 dropouts overfitting is reduced and theproposed DeepLSTM model performs better in terms ofprediction )e ldquotanhrdquo function is used as an activationfunction and generates the output of 64 units

StimuliEEG Data

AcquisitionFeature

ExtractionDeepLSTM

Model

DataClassification

PersonalityPrediction

f1f2f3f10

EEG Signals AreGenerated While

Watching

E vs IS vs IT vs FJ vs P

Figure 3 Personality prediction framework using EEG signals and DeepLSTM model

4 Computational Intelligence and Neuroscience

tanh(x) 2

1 + eminus2x

minus 1 (1)

ldquoSoftmaxrdquo is used as an activation function in the lastlayer and 4 outputs are generated representing four per-sonality classes )e key benefit of using the softmax as anactivation function is the range of output probabilitieswhich will be between 0 and 1 It returns each classrsquosprobabilities with the target class having the highestprobability

Softmax xi( 1113857 exp xi( 1113857

1113936jexp xj1113872 1113873 (2)

LSTM cells and dropout layers are utilised to discoverthe role of EEG signals )e overfitting of these systems wasminimized by restricting unit coadapting in the dropoutlayer of our DeepLSTM architectures )e dense layer theloss function for these network architectures is categoricalcross-entropy and the batch size is 40 )e adaptive momentestimation optimizer (Adam) is used for a learning rate of0001 )e normalization is applied to the dataset inputfeatures with the MinMaxScaler function after loading thedataset )is function normalizes each feature because ofwhich each feature contributes in a maintained manner Itdecreases the internal covariate transition resulting in achange in network activation distribution due to shifts innetwork parameters during training )e normalization ofthe proposed network enhances training reducing thechange in the internal covariance It also helped improve theoptimization phase by stopping weights from burstingaround the entire site by limiting them to a specific set Anundesired advantage of normalization is that it often allowsthe mechanism to regularize somewhat In the parameterspecified by Table 2 the proposed DeepLSTM network isinitialized [29] We test the output of our proposedDeepLSTMmodel which classifies EEG signals as an outputvalue into five personality groups for 500 epochs with a

batch size of 40 )e DeepLSTM model was evaluated usingthe suggested EEG dataset as well as the publicly availableASCERTAIN EEG dataset

)e proposed architectures and parameters were chosenbased on our own experiments with nearby architectures (interms of layers and nodes) In terms of accuracy the pro-posed DeepLSTM architecture outperforms their nearbyarchitecture

5 Experimental Results

)e results of the DeepLSTM model for classifying the EEGsignals and to check our systemrsquos efficacy are presented next)e computer environment is composed of 34GHz devotedto the 32GB RAM-based Python (36) to incorporateDeepLSTM cell architecture and other states of the art ieANN KNN LibSVM and HGP)e parameter values of theANN KNN LibSVM and HGP are the same as in [19 30]respectively )e parameter values taken for the imple-mentation of the DeepLSTM model are given in Table 2

)e dataset is typically split into two distinct sets ietraining sets and test sets A general review of our method iscarried out in this research of personality trait classificationusing EEG signals We have separated the dataset intodifferent training and testing partitions to equate them withexisting literature)e performance assessment is conductedusing a 50ndash50 60ndash40 70ndash30 and 10-fold partition scheme

512 Memory Unit

256 Memory Unit

128 Memory Unit

64 Memory Unit

Input Features

LSTM Layer 1

LSTM Layer 2

LSTM Layer 3

Tanh

SoMax4 Output

DROPOUT LAYER

DROPOUT LAYER

DROPOUT LAYER

DeepLSTM

F1 F2 F3 F10

Figure 4 DeepLSTM cells network architecture

Table 2 DeepLSTM model formation parameter values

Parameter ValueOptimizer AdamRate of learning 0001Rate of dropout 02Loss function Categorical cross-entropyMetrics used AccuracySize of batch 40

Computational Intelligence and Neuroscience 5

In 50ndash50 60ndash40 and 70ndash30 training-testing partition50 60 and 70 respectively data is used for trainingand 50 40 and 30 respectively of the data is used fortesting )e complete dataset is partitioned into approxi-mately ten equal size blocks in a 10-fold cross-validationscheme 90 of the dataset ie nine blocks becomes ourtraining data and 10 of the dataset ie one block becomesour testing data)is process is repeated ten times with eachtime a different data block being used for testing Also ourproposed modelrsquos sensitivity precision and specificity valuefor the 50ndash50 60ndash40 70ndash30 and 10-fold partition schemesare calculated

51 DeepLSTM Architecture Evaluation )is study uses adeep learning algorithm to distinguish personality traitsfrom EEG signals In practice the DeepLSTM model out-performs traditional machine learning algorithms because ithas the capability of remembering the long-term depen-dence of sequential data in time increasing the likelihood ofcorrectness in a short period of time [31]

Table 3 represents the classification accuracy comparisonfor personality prediction DeepLSTM model on the AS-CERTAIN and the proposed EEG datasets For the AS-CERTAIN and the proposed EEG datasets the proposedDeepLSTM model maximum average and minimumclassification accuracy for 50ndash50 60ndash40 70ndash30 and 10-foldcross-validation partition scheme is calculated

)e maximum classification accuracy of the proposedDeepLSTM model for 50ndash50 60ndash40 70ndash30 and 10-foldcross-validation partition scheme on the ASCERTAIN EEGdataset is 8248 8814 9286 and 9532 respectively

)e maximum classification accuracy of the proposedDeepLSTM model for 50ndash50 60ndash40 70ndash30 and 10-foldcross-validation partition scheme on the proposed EEGdataset is 8456 9152 9482 and 9694 respectivelyFrom the results it can be seen that the DeepLSTM modelperforms better in terms of performance on the ASCER-TAIN and the proposed EEG datasets and the classificationaccuracy of the DeepLSTMmodel is higher on our proposedEEG dataset than the ASCERTAIN dataset

6 Discussion

)is section discusses how the proposed deep learning-basedDeepLSTM model works compared to conventional ma-chine learning algorithms

A comparison with standard conventional classificationalgorithms is carried out using the same collection of fea-tures as used in DeepLSTM-based methodology to show theadvantages of incorporating deep learning into the classi-fication of personality traits

)e KNN ANN LibSVM and HGP are the other state-of-the-art approaches used for comparison )e classifica-tion accuracy comparison of personality traits is containedin Table 4 It contains the maximum average and minimumaccuracy for the 50ndash50 60ndash40 70ndash30 and 10-fold partitionschemes )e parameters and settings for these variableshave all been implemented using the same technique toensure that the findings and comparisons offered are un-ambiguous and consistent

)e proposed deep learning approach has a greaterimpact than traditional machine learning algorithms )eDeepLSTM classification improved dramatically in clas-sification accuracy as per the results Besides the rise inclassification accuracy the DeepLSTM classifier can alsoretain specificity greater than 9286 on the ASCERTAINdataset and 9384 on the proposed EEG dataset resultingin very low false prediction rates )e sensitivity value ofthe DeepLSTM model for the ASCERTAIN dataset is9472 and the proposed EEG dataset is 9586 and ishigh in the other state-of-the-art methods which showsthat the DeepLSTM model correctly classifies the minorityclass samples )e precision value of the DeepLSTMmodelfor the ASCERTAIN dataset is 9348 and the proposedEEG dataset is 9444 and is high in the other state-of-the-art methods )e F1 score value of the DeepLSTM modelfor the ASCERTAIN dataset is 9368 and the proposedEEG dataset is 9496 and is high in the other state-of-the-art methods Table 5 shows the relation of sensitivityprecision specificity and F1 score values of DeepLSTM for50ndash50 60ndash40 70ndash30 and 10-fold data partitioningscheme

Table 6 shows the statistical result disparity is illustratedby the two-tailed MannndashWhitney test [32] )e Man-nndashWhitney test is used to compute the p value relation inclassification accuracy )e outcomes do not change sig-nificantly if the p value is greater than 005 and it is highly

Table 3 Classification accuracy comparison for personality pre-diction of DeepLSTM model on the ASCERTAIN and proposedEEG datasets

Dataset Method Validationtechnique

AccuracyMax Avg Min

ASCERTAIN DeepLSTMclassifier

50ndash50 8248 8036 774260ndash40 8814 8563 814670ndash30 9286 8968 864610-fold 9532 9416 9198

Proposeddataset

DeepLSTMclassifier

50ndash50 8456 8244 796260ndash40 9152 8762 848670ndash30 9482 9168 887210-fold 9694 9588 9394

Optimal values are represented in bold

6 Computational Intelligence and Neuroscience

Table 5 Comparison of sensitivity precision and specificity of DeepLSTM model for various partition schemes

Dataset Validation techniqueSensitivity () Precision () Specificity () F1 score ()Meanplusmn Std Meanplusmn Std Meanplusmn Std Meanplusmn Std

ASCERTAIN

50ndash50 8164 plusmn308 8053 plusmn312 7842 plusmn284 8056 plusmn23660ndash40 8776 plusmn314 8682 plusmn324 8594 plusmn268 8644 plusmn24470ndash30 9149 plusmn316 9056 plusmn342 8967 plusmn236 9040 plusmn34210-fold 9472 plusmn 316 9348 plusmn 312 9286 plusmn 298 9368 plusmn 284

Proposed dataset

50ndash50 8346 plusmn314 8275 plusmn315 8114 plusmn324 8246 plusmn31860ndash40 9074 plusmn324 8925 plusmn316 8894 plusmn318 8946 plusmn33270ndash30 9354 plusmn314 9285 plusmn325 9194 plusmn342 9228 plusmn32410-fold 9586 plusmn 318 9444 plusmn 314 9384 plusmn 312 9496 plusmn 316

Optimal values are represented in bold

Table 4 Classification accuracy comparison for personality prediction of DeepLSTM model with other state-of-the-art algorithms on theASCERTAIN and the proposed EEG dataset

Dataset Method Validation techniqueAccuracy

Max Avg Min

ASCERTAIN

ANN 50ndash50 7084 6724 6448KNN 50ndash50 6764 6426 6223

LIBSVM 50ndash50 7728 7384 7186HGP 50ndash50 7852 7538 7268

DeepLSTM classifier 50ndash50 8248 8036 7742

ASCERTAIN

ANN 60ndash40 7434 6986 6774KNN 60ndash40 7038 6816 6568

LIBSVM 60ndash40 7986 7728 7546HGP 60ndash40 8127 7873 7608

DeepLSTM classifier 60ndash40 8814 8563 8146

ASCERTAIN

ANN 70ndash30 7518 7316 6994KNN 70ndash30 7282 7084 6862

LIBSVM 70ndash30 8326 8162 7986HGP 70ndash30 8664 8338 8074

DeepLSTM classifier 70ndash30 9286 8968 8646

ASCERTAIN

ANN 10-fold 7882 7464 7246KNN 10-fold 7436 7237 7025

LIBSVM 10-fold 8482 8242 8028HGP 10-fold 8612 8386 8184

DeepLSTM classifier 10-fold 9532 9416 9198

Proposed dataset

ANN 50ndash50 7284 6936 6648KNN 50ndash50 6932 6692 6386

LIBSVM 50ndash50 7974 7664 7386HGP 50ndash50 8036 7783 7489

DeepLSTM classifier 50ndash50 8456 8244 7962

Proposed dataset

ANN 60ndash40 7616 7328 7012KNN 60ndash40 7264 7062 6854

LIBSVM 60ndash40 8126 7958 7782HGP 60ndash40 8328 8094 7863

DeepLSTM classifier 60ndash40 9152 8762 8486

proposed dataset

ANN 70ndash30 7862 7394 7128KNN 70ndash30 7468 7236 7082

LIBSVM 70ndash30 8564 8370 8158HGP 70ndash30 8769 8474 8284

DeepLSTM classifier 70ndash30 9482 9168 8872

Proposed dataset

ANN 10-fold 8024 7654 7498KNN 10-fold 7682 7464 7228

LIBSVM 10-fold 8804 8516 8326HGP 10-fold 9032 8693 8478

DeepLSTM classifier 10-fold 9694 9588 9394Optimal values are represented in bold

Computational Intelligence and Neuroscience 7

Tabl

e6

pvaluecomparisonforDeepL

STM

usingMannndash

Whitney

test

Training

-testin

gANN

KNN

LibSVM

HGP

Partition

pvalue

Sign

ificance

p-value

Sign

ificance

pvalue

Sign

ificance

pvalue

Sign

ificance

DeepL

STM

50ndash5

02681x

10minus11

Highly

Sign

ificant

1450x

10minus11

Highly

Sign

ificant

19543

10minus11

Highly

Sign

ificant

18563

10minus11

Highly

Sign

ificant

60ndash4

02634x

10minus11

Highly

Sign

ificant

1350x

10minus11

Highly

Sign

ificant

18703

10minus11

Highly

Sign

ificant

18363

10minus11

Highly

Sign

ificant

70ndash3

02576x

10minus11

Highly

Sign

ificant

1270x

10minus11

Highly

Sign

ificant

18423

10minus11

Highly

Sign

ificant

17946

10minus11

Highly

Sign

ificant

10-fold

2486x

10minus11

Highly

Sign

ificant

1235x

10minus11

Highly

Sign

ificant

17856

10minus11

Highly

Sign

ificant

17637

10minus11

Highly

Sign

ificant

Optim

alvalues

arerepresentedin

bold

8 Computational Intelligence and Neuroscience

significant if the p value is less than 0001 It is evident fromthe interventions in Table 6 that the solution provided by ourproposed DeepLSTM model is statistically different fromANN KNN LibSVM andHGP for the 50ndash50 60ndash40 70ndash30and 10-fold data partitioning scheme When the p values arecontrasted with DeepLSTM for these classifiers there is asignificant variation in outcomes )e evaluation resultssuggest that the proposed DeepLSTM-based deep learningmodel for classifying personality traits provides accurateclassification results

7 Conclusion

During this study we propose EEG signals-based personalityprediction system using DeepLSTM-based deep learningmodel

A new EEG dataset was also created using 40 film clips ofHindi and English languages )e proposed DeepLSTMmodel was also applied to the publicly available EEG datasetknown as ASCERTAIN Multiple experiments have beencarried out to validate our results which are helpful tocompare our DeepLSTMmodel with existing methods Fiftyparticipants were involved and saw a few movie clips tar-geting eight different personality traits )is method usesNeuroSky MindWave mobile 2 to capture brain signalsBetter results of sensitivity precision and specificity indicatethat our approach beats the current literature )e classi-fication accuracy of the proposed DeepLSTM model on ourproposed EEG dataset is 9694 for the 10-fold partitionscheme and outperforms the results of the DeepLSTMmodel on the ASCERTAIN dataset having classificationaccuracy of 9532

We are currently using a single-channel device and inthe future we will extend it to multichannel devices

Data Availability

)e data are available on request from the correspondingauthor

Conflicts of Interest

)e authors declare that they have no conflicts of interest

References

[1] D P McAdams and B D Olson ldquoPersonality developmentcontinuity and change over the life courserdquo Annual Review ofPsychology vol 61 no 1 pp 517ndash542 2010

[2] O P John S E Hampson and L R Goldberg ldquo)e basic levelin personality-trait hierarchies studies of trait use and ac-cessibility in different contextsrdquo Journal of Personality andSocial Psychology vol 60 no 3 pp 348ndash361 1991

[3] F M Cheung and K Leung ldquoIndigenous personality mea-suresrdquo Journal of Cross-Cultural Psychology vol 29 no 1pp 233ndash248 1998

[4] R Harrington and D A Loffredo ldquoMbti personality type andother factors that relate to preference for online versus face-to-face instructionrdquo e Internet and Higher Educationvol 13 no 1-2 pp 89ndash95 2010

[5] R R McCrae and O P John ldquoAn introduction to the five-factor model and its applicationsrdquo Journal of Personalityvol 60 no 2 pp 175ndash215 1992

[6] S K Bhatti A Muneer M I Lali M Gull and S M U DinldquoPersonality analysis of the USA public using twitter profilepicturesrdquo in Proceedings of the 2017 International Conferenceon Information and Communication Technologies (ICICT)pp 165ndash172 Karachi Pakistan December 2017

[7] Y Gvili O Kol and S Levy ldquo)e value (s) of information onsocial network sites the role of user personality traitsrdquo Eu-ropean Review of Applied Psychology vol 70 Article ID100511 2019

[8] A Eftekhar C Fullwood and N Morris ldquoCapturing per-sonality from facebook photos and photo-related activitieshow much exposure do you needrdquo Computers in HumanBehavior vol 37 pp 162ndash170 2014

[9] U Oberst V Renau A Chamarro and X Carbonell ldquoGenderstereotypes in facebook profiles are women more femaleonlinerdquo Computers in Human Behavior vol 60 pp 559ndash5642016

[10] Y-C J Wu W-H Chang and C-H Yuan ldquoDo Facebookprofile pictures reflect userrsquos personalityrdquo Computers inHuman Behavior vol 51 pp 880ndash889 2015

[11] G Stemmler and J Wacker ldquoPersonality emotion and in-dividual differences in physiological responsesrdquo BiologicalPsychology vol 84 no 3 pp 541ndash551 2010

[12] A Baumert M Schmitt and M Perugini ldquoTowards an ex-planatory personality psychology integrating personalitystructure personality process and personality developmentrdquoPersonality and Individual Differences vol 147 pp 18ndash272019

[13] S Kreitler ldquoTowards a consensual model in personalitypsychologyrdquo Personality and Individual Differences vol 147pp 156ndash165 2019

[14] C Montag and J D Elhai ldquoA new agenda for personalitypsychology in the digital agerdquo Personality and IndividualDifferences vol 147 pp 128ndash134 2019

[15] R Akhtar D Winsborough U Ort A Johnson andT Chamorro-Premuzic ldquoDetecting the dark side of per-sonality using social media status updatesrdquo Personality andIndividual Differences vol 132 pp 90ndash97 2018

[16] A C E S Lima and L N De Castro ldquoA multi-label semi-supervised classification approach applied to personalityprediction in social mediardquo Neural Networks vol 58pp 122ndash130 2014

[17] A Bhardwaj A Tiwari M V Varma and M R KrishnaldquoClassification of eeg signals using a novel genetic pro-gramming approachrdquo in Proceedings of the CompanionPublication of the 2014 Annual Conference on Genetic andEvolutionary Computation pp 1297ndash1304 Ireland 2014

[18] H Bhardwaj A Sakalle A Bhardwaj and A TiwarildquoClassification of electroencephalogram signal for the de-tection of epilepsy using innovative genetic programmingrdquoExpert Systems vol 36 no 1 Article ID e12338 2019

[19] A Bhardwaj A Tiwari M V Varma and M R Krishna ldquoAnanalysis of integration of hill climbing in crossover andmutation operation for eeg signal classificationrdquo in Pro-ceedings of the 2015 Annual Conference on Genetic andEvolutionary Computation pp 209ndash216 ACM YangonMyanmar August 2015

[20] T Takahashi T Murata T Hamada et al ldquoChanges in eegand autonomic nervous activity during meditation and theirassociation with personality traitsrdquo International Journal ofPsychophysiology vol 55 no 2 pp 199ndash207 2005

Computational Intelligence and Neuroscience 9

[21] H Bhardwaj P Tomar A Sakalle and A Bhardwaj ldquoClas-sification of extraversion and introversion personality traitusing electroencephalogram signalsrdquo Communications inComputer and Information Science vol 1434 pp 31ndash39 2021

[22] A Sakalle P Tomar H Bhardwaj and A BhardwajldquoEmotion recognition using portable eeg devicerdquo in Com-munications in Computer and Information Science pp 17ndash30Springer International Publishing Berlin Germany 2021

[23] A Kumar N Sinha and A Bhardwaj ldquoA novel fitnessfunction in genetic programming for medical data classifi-cationrdquo Journal of Biomedical Informatics vol 112 Article ID103623 2020

[24] A Purohit A Bhardwaj A Tiwari and N S ChoudharildquoRemoving code bloating in crossover operation in geneticprogrammingrdquo in Proceedings of the 2011 InternationalConference on Recent Trends in Information Technology(ICRTIT) pp 1126ndash1130 Chennai 2011

[25] S Fayolle S Droit-Volet and S Gil ldquoEmotion and timeperception effects of film-induced moodrdquo Procedia - Socialand Behavioral Sciences vol 126 pp 251-252 2014

[26] R Subramanian J Wache M K Abadi R L VieriuS Winkler and N Sebe ldquoAscertain emotion and personalityrecognition using commercial sensorsrdquo IEEE Transactions onAffective Computing vol 9 pp 147ndash160 2016

[27] A Caspi B W Roberts and R L Shiner ldquoPersonality de-velopment stability and changerdquo Annual Review of Psy-chology vol 56 no 1 pp 453ndash484 2005

[28] W Zaremba I Sutskever and O Vinyals ldquoRecurrent neuralnetwork regularizationrdquo 2014 httpsarxivorgabs14092329

[29] A Sakalle P Tomar H Bhardwaj D Acharya andA Bhardwaj ldquoA lstm based deep learning network for rec-ognizing emotions using wireless brainwave driven systemrdquoExpert Systems with Applications vol 173 Article ID 1145162021

[30] A M Bhatti M Majid S M Anwar and B Khan ldquoHumanemotion recognition and analysis in response to audio musicusing brain signalsrdquo Computers in Human Behavior vol 65pp 267ndash275 2016

[31] Y Bengio P Simard and P Frasconi ldquoLearning long-termdependencies with gradient descent is difficultrdquo IEEETransactions on Neural Networks vol 5 no 2 pp 157ndash1661994

[32] H B Mann and D R Whitney ldquoOn a test of whether one oftwo random variables is stochastically larger than the otherrdquoeAnnals of Mathematical Statistics vol 18 no 1 pp 50ndash601947

10 Computational Intelligence and Neuroscience

dataset Single-channel EEG adjustable headband was usedto monitor the EEG signals

After viewing a film clip of one trait the participants hadto fill the self-evaluation form with options ldquoagreerdquo ldquoneu-tralrdquo or ldquodisagreerdquo and have seven questionnaires for eachpersonality trait )ese questionnaires are constructed bytargeting the characteristics of personality traits )esequestionnaires must be answered based on the participantsrsquoreal feelings instead of their typical emotions or generalattitude which may differ from person to person Because ofthat the answer to those questionnaires may differ In eachclip a 1-minute buffer is for neutral clip to neutralize theparticipantsrsquo elicited personality traits After all of thequestions for each of the four grouped personality traits havebeen answered the questionnaire (which contains sevenquestions) is evaluated for each participantrsquos traits )elabeling of the EEG signal depends on the output of thequestionnaires given by the participant )e final output isevaluated by the following procedure Let us suppose that theparticipant has watched the film clip targeting the charac-teristic of the extraversion trait After watching the film clipthe participant answered the questionnaires based on theextraversion trait Suppose the participant selects for theldquoagreerdquo option in the questionnaire In that case we can raiseit by value 1 If for the extraversion questionnaire theparticipant chooses the option ldquodisagreerdquo we raise thecounter of the introversion trait (versus trait of extraversion)by one If the participant opts for the neutral option weneither increase nor decrease the counter for any trait Sincethere are seven extraversion trait questionnaires the EEGsignal labeling depends on the participantrsquos output andthree labeling possibilities exist

(i) )e EEG signal is labeled as extraversion if thenumber of ldquoagreerdquo options is more selected thanldquodisagreerdquo

(ii) )e EEG signal is labeled as introversion if thenumber of ldquodisagreerdquo options is more selected thanldquoagreerdquo

(iii) )e EEG signal is discarded if the number of ldquoagreerdquoand ldquodisagreerdquo options are equal in number

Similarly for introversion trait-based questionnaires thecounter for introversion trait is incremented if the partici-pant chooses the ldquoagreerdquo option If the participant choosesthe ldquodisagreerdquo option the counter for extraversion isincremented If the participant opts for the neutral optionwe do not increase or diminish the counter for that

questionnaire of both the traits )e labeling of the EEGsignal is done by following the above procedure Similarlythe remaining personality traits marking is done and theirrelated EEG signals are labeled )e same experimentalprocedure is repeated after three days for collecting the dataand removing bias

At the end of each traitrsquos evaluation process the datasetrsquosmaximum counter value is labeled To label the EEG signalsthis marking scheme is taken as the reference )e studyrsquostesting data were obtained using just four video clips tar-geting one personality trait from each group

)e experiment will be performed using four machinelearning algorithms ANN KNN LibSVM and HGP in-cluding our proposed DeepLSTM classifier )e survey andreview of results for the recorded EEG signal dataset usingthe described machine learning algorithms will providevaluable material for a similar study of personality types)ese findings show that personality inference from EEGsignals outperforms state-of-the-art clear behavioral indi-cators in classification accuracy

42 Proposed DeepLSTM Model Various algorithms forlearning machines are used for the recognition and de-scription of personality characteristics in literature )eDeepLSTM model for personality traits classification withthe use of EEG signals is used in this work

Figure 4 includes the architectures of the DeepLSTM cellnetwork used for classifying the personality traits by usingEEG signals in this analysis )e DeepLSTM network hasbeen established on the backend in Python 36 Keras 209 onTensorFlow 140

In DeepLSTM architecture there are 3 LSTM layerswith 512 memory units in the first layer 256 memory unitsin the second layer and 128 memory units in the thirdlayer In all proposed architectures the dropout layer isalso used and the probability value is 02 In the modelbetween existing layers the dropout layer is applied toprevious layer outputs which are fed to the layer asshown in Figure 4 A layerrsquos outputs are arbitrarily sub-sampled under dropout layer )e memorization capa-bility of the DeepLSTM model is due to the dropoutregularization [28] Furthermore the model is trainedfaster with the 02 dropouts overfitting is reduced and theproposed DeepLSTM model performs better in terms ofprediction )e ldquotanhrdquo function is used as an activationfunction and generates the output of 64 units

StimuliEEG Data

AcquisitionFeature

ExtractionDeepLSTM

Model

DataClassification

PersonalityPrediction

f1f2f3f10

EEG Signals AreGenerated While

Watching

E vs IS vs IT vs FJ vs P

Figure 3 Personality prediction framework using EEG signals and DeepLSTM model

4 Computational Intelligence and Neuroscience

tanh(x) 2

1 + eminus2x

minus 1 (1)

ldquoSoftmaxrdquo is used as an activation function in the lastlayer and 4 outputs are generated representing four per-sonality classes )e key benefit of using the softmax as anactivation function is the range of output probabilitieswhich will be between 0 and 1 It returns each classrsquosprobabilities with the target class having the highestprobability

Softmax xi( 1113857 exp xi( 1113857

1113936jexp xj1113872 1113873 (2)

LSTM cells and dropout layers are utilised to discoverthe role of EEG signals )e overfitting of these systems wasminimized by restricting unit coadapting in the dropoutlayer of our DeepLSTM architectures )e dense layer theloss function for these network architectures is categoricalcross-entropy and the batch size is 40 )e adaptive momentestimation optimizer (Adam) is used for a learning rate of0001 )e normalization is applied to the dataset inputfeatures with the MinMaxScaler function after loading thedataset )is function normalizes each feature because ofwhich each feature contributes in a maintained manner Itdecreases the internal covariate transition resulting in achange in network activation distribution due to shifts innetwork parameters during training )e normalization ofthe proposed network enhances training reducing thechange in the internal covariance It also helped improve theoptimization phase by stopping weights from burstingaround the entire site by limiting them to a specific set Anundesired advantage of normalization is that it often allowsthe mechanism to regularize somewhat In the parameterspecified by Table 2 the proposed DeepLSTM network isinitialized [29] We test the output of our proposedDeepLSTMmodel which classifies EEG signals as an outputvalue into five personality groups for 500 epochs with a

batch size of 40 )e DeepLSTM model was evaluated usingthe suggested EEG dataset as well as the publicly availableASCERTAIN EEG dataset

)e proposed architectures and parameters were chosenbased on our own experiments with nearby architectures (interms of layers and nodes) In terms of accuracy the pro-posed DeepLSTM architecture outperforms their nearbyarchitecture

5 Experimental Results

)e results of the DeepLSTM model for classifying the EEGsignals and to check our systemrsquos efficacy are presented next)e computer environment is composed of 34GHz devotedto the 32GB RAM-based Python (36) to incorporateDeepLSTM cell architecture and other states of the art ieANN KNN LibSVM and HGP)e parameter values of theANN KNN LibSVM and HGP are the same as in [19 30]respectively )e parameter values taken for the imple-mentation of the DeepLSTM model are given in Table 2

)e dataset is typically split into two distinct sets ietraining sets and test sets A general review of our method iscarried out in this research of personality trait classificationusing EEG signals We have separated the dataset intodifferent training and testing partitions to equate them withexisting literature)e performance assessment is conductedusing a 50ndash50 60ndash40 70ndash30 and 10-fold partition scheme

512 Memory Unit

256 Memory Unit

128 Memory Unit

64 Memory Unit

Input Features

LSTM Layer 1

LSTM Layer 2

LSTM Layer 3

Tanh

SoMax4 Output

DROPOUT LAYER

DROPOUT LAYER

DROPOUT LAYER

DeepLSTM

F1 F2 F3 F10

Figure 4 DeepLSTM cells network architecture

Table 2 DeepLSTM model formation parameter values

Parameter ValueOptimizer AdamRate of learning 0001Rate of dropout 02Loss function Categorical cross-entropyMetrics used AccuracySize of batch 40

Computational Intelligence and Neuroscience 5

In 50ndash50 60ndash40 and 70ndash30 training-testing partition50 60 and 70 respectively data is used for trainingand 50 40 and 30 respectively of the data is used fortesting )e complete dataset is partitioned into approxi-mately ten equal size blocks in a 10-fold cross-validationscheme 90 of the dataset ie nine blocks becomes ourtraining data and 10 of the dataset ie one block becomesour testing data)is process is repeated ten times with eachtime a different data block being used for testing Also ourproposed modelrsquos sensitivity precision and specificity valuefor the 50ndash50 60ndash40 70ndash30 and 10-fold partition schemesare calculated

51 DeepLSTM Architecture Evaluation )is study uses adeep learning algorithm to distinguish personality traitsfrom EEG signals In practice the DeepLSTM model out-performs traditional machine learning algorithms because ithas the capability of remembering the long-term depen-dence of sequential data in time increasing the likelihood ofcorrectness in a short period of time [31]

Table 3 represents the classification accuracy comparisonfor personality prediction DeepLSTM model on the AS-CERTAIN and the proposed EEG datasets For the AS-CERTAIN and the proposed EEG datasets the proposedDeepLSTM model maximum average and minimumclassification accuracy for 50ndash50 60ndash40 70ndash30 and 10-foldcross-validation partition scheme is calculated

)e maximum classification accuracy of the proposedDeepLSTM model for 50ndash50 60ndash40 70ndash30 and 10-foldcross-validation partition scheme on the ASCERTAIN EEGdataset is 8248 8814 9286 and 9532 respectively

)e maximum classification accuracy of the proposedDeepLSTM model for 50ndash50 60ndash40 70ndash30 and 10-foldcross-validation partition scheme on the proposed EEGdataset is 8456 9152 9482 and 9694 respectivelyFrom the results it can be seen that the DeepLSTM modelperforms better in terms of performance on the ASCER-TAIN and the proposed EEG datasets and the classificationaccuracy of the DeepLSTMmodel is higher on our proposedEEG dataset than the ASCERTAIN dataset

6 Discussion

)is section discusses how the proposed deep learning-basedDeepLSTM model works compared to conventional ma-chine learning algorithms

A comparison with standard conventional classificationalgorithms is carried out using the same collection of fea-tures as used in DeepLSTM-based methodology to show theadvantages of incorporating deep learning into the classi-fication of personality traits

)e KNN ANN LibSVM and HGP are the other state-of-the-art approaches used for comparison )e classifica-tion accuracy comparison of personality traits is containedin Table 4 It contains the maximum average and minimumaccuracy for the 50ndash50 60ndash40 70ndash30 and 10-fold partitionschemes )e parameters and settings for these variableshave all been implemented using the same technique toensure that the findings and comparisons offered are un-ambiguous and consistent

)e proposed deep learning approach has a greaterimpact than traditional machine learning algorithms )eDeepLSTM classification improved dramatically in clas-sification accuracy as per the results Besides the rise inclassification accuracy the DeepLSTM classifier can alsoretain specificity greater than 9286 on the ASCERTAINdataset and 9384 on the proposed EEG dataset resultingin very low false prediction rates )e sensitivity value ofthe DeepLSTM model for the ASCERTAIN dataset is9472 and the proposed EEG dataset is 9586 and ishigh in the other state-of-the-art methods which showsthat the DeepLSTM model correctly classifies the minorityclass samples )e precision value of the DeepLSTMmodelfor the ASCERTAIN dataset is 9348 and the proposedEEG dataset is 9444 and is high in the other state-of-the-art methods )e F1 score value of the DeepLSTM modelfor the ASCERTAIN dataset is 9368 and the proposedEEG dataset is 9496 and is high in the other state-of-the-art methods Table 5 shows the relation of sensitivityprecision specificity and F1 score values of DeepLSTM for50ndash50 60ndash40 70ndash30 and 10-fold data partitioningscheme

Table 6 shows the statistical result disparity is illustratedby the two-tailed MannndashWhitney test [32] )e Man-nndashWhitney test is used to compute the p value relation inclassification accuracy )e outcomes do not change sig-nificantly if the p value is greater than 005 and it is highly

Table 3 Classification accuracy comparison for personality pre-diction of DeepLSTM model on the ASCERTAIN and proposedEEG datasets

Dataset Method Validationtechnique

AccuracyMax Avg Min

ASCERTAIN DeepLSTMclassifier

50ndash50 8248 8036 774260ndash40 8814 8563 814670ndash30 9286 8968 864610-fold 9532 9416 9198

Proposeddataset

DeepLSTMclassifier

50ndash50 8456 8244 796260ndash40 9152 8762 848670ndash30 9482 9168 887210-fold 9694 9588 9394

Optimal values are represented in bold

6 Computational Intelligence and Neuroscience

Table 5 Comparison of sensitivity precision and specificity of DeepLSTM model for various partition schemes

Dataset Validation techniqueSensitivity () Precision () Specificity () F1 score ()Meanplusmn Std Meanplusmn Std Meanplusmn Std Meanplusmn Std

ASCERTAIN

50ndash50 8164 plusmn308 8053 plusmn312 7842 plusmn284 8056 plusmn23660ndash40 8776 plusmn314 8682 plusmn324 8594 plusmn268 8644 plusmn24470ndash30 9149 plusmn316 9056 plusmn342 8967 plusmn236 9040 plusmn34210-fold 9472 plusmn 316 9348 plusmn 312 9286 plusmn 298 9368 plusmn 284

Proposed dataset

50ndash50 8346 plusmn314 8275 plusmn315 8114 plusmn324 8246 plusmn31860ndash40 9074 plusmn324 8925 plusmn316 8894 plusmn318 8946 plusmn33270ndash30 9354 plusmn314 9285 plusmn325 9194 plusmn342 9228 plusmn32410-fold 9586 plusmn 318 9444 plusmn 314 9384 plusmn 312 9496 plusmn 316

Optimal values are represented in bold

Table 4 Classification accuracy comparison for personality prediction of DeepLSTM model with other state-of-the-art algorithms on theASCERTAIN and the proposed EEG dataset

Dataset Method Validation techniqueAccuracy

Max Avg Min

ASCERTAIN

ANN 50ndash50 7084 6724 6448KNN 50ndash50 6764 6426 6223

LIBSVM 50ndash50 7728 7384 7186HGP 50ndash50 7852 7538 7268

DeepLSTM classifier 50ndash50 8248 8036 7742

ASCERTAIN

ANN 60ndash40 7434 6986 6774KNN 60ndash40 7038 6816 6568

LIBSVM 60ndash40 7986 7728 7546HGP 60ndash40 8127 7873 7608

DeepLSTM classifier 60ndash40 8814 8563 8146

ASCERTAIN

ANN 70ndash30 7518 7316 6994KNN 70ndash30 7282 7084 6862

LIBSVM 70ndash30 8326 8162 7986HGP 70ndash30 8664 8338 8074

DeepLSTM classifier 70ndash30 9286 8968 8646

ASCERTAIN

ANN 10-fold 7882 7464 7246KNN 10-fold 7436 7237 7025

LIBSVM 10-fold 8482 8242 8028HGP 10-fold 8612 8386 8184

DeepLSTM classifier 10-fold 9532 9416 9198

Proposed dataset

ANN 50ndash50 7284 6936 6648KNN 50ndash50 6932 6692 6386

LIBSVM 50ndash50 7974 7664 7386HGP 50ndash50 8036 7783 7489

DeepLSTM classifier 50ndash50 8456 8244 7962

Proposed dataset

ANN 60ndash40 7616 7328 7012KNN 60ndash40 7264 7062 6854

LIBSVM 60ndash40 8126 7958 7782HGP 60ndash40 8328 8094 7863

DeepLSTM classifier 60ndash40 9152 8762 8486

proposed dataset

ANN 70ndash30 7862 7394 7128KNN 70ndash30 7468 7236 7082

LIBSVM 70ndash30 8564 8370 8158HGP 70ndash30 8769 8474 8284

DeepLSTM classifier 70ndash30 9482 9168 8872

Proposed dataset

ANN 10-fold 8024 7654 7498KNN 10-fold 7682 7464 7228

LIBSVM 10-fold 8804 8516 8326HGP 10-fold 9032 8693 8478

DeepLSTM classifier 10-fold 9694 9588 9394Optimal values are represented in bold

Computational Intelligence and Neuroscience 7

Tabl

e6

pvaluecomparisonforDeepL

STM

usingMannndash

Whitney

test

Training

-testin

gANN

KNN

LibSVM

HGP

Partition

pvalue

Sign

ificance

p-value

Sign

ificance

pvalue

Sign

ificance

pvalue

Sign

ificance

DeepL

STM

50ndash5

02681x

10minus11

Highly

Sign

ificant

1450x

10minus11

Highly

Sign

ificant

19543

10minus11

Highly

Sign

ificant

18563

10minus11

Highly

Sign

ificant

60ndash4

02634x

10minus11

Highly

Sign

ificant

1350x

10minus11

Highly

Sign

ificant

18703

10minus11

Highly

Sign

ificant

18363

10minus11

Highly

Sign

ificant

70ndash3

02576x

10minus11

Highly

Sign

ificant

1270x

10minus11

Highly

Sign

ificant

18423

10minus11

Highly

Sign

ificant

17946

10minus11

Highly

Sign

ificant

10-fold

2486x

10minus11

Highly

Sign

ificant

1235x

10minus11

Highly

Sign

ificant

17856

10minus11

Highly

Sign

ificant

17637

10minus11

Highly

Sign

ificant

Optim

alvalues

arerepresentedin

bold

8 Computational Intelligence and Neuroscience

significant if the p value is less than 0001 It is evident fromthe interventions in Table 6 that the solution provided by ourproposed DeepLSTM model is statistically different fromANN KNN LibSVM andHGP for the 50ndash50 60ndash40 70ndash30and 10-fold data partitioning scheme When the p values arecontrasted with DeepLSTM for these classifiers there is asignificant variation in outcomes )e evaluation resultssuggest that the proposed DeepLSTM-based deep learningmodel for classifying personality traits provides accurateclassification results

7 Conclusion

During this study we propose EEG signals-based personalityprediction system using DeepLSTM-based deep learningmodel

A new EEG dataset was also created using 40 film clips ofHindi and English languages )e proposed DeepLSTMmodel was also applied to the publicly available EEG datasetknown as ASCERTAIN Multiple experiments have beencarried out to validate our results which are helpful tocompare our DeepLSTMmodel with existing methods Fiftyparticipants were involved and saw a few movie clips tar-geting eight different personality traits )is method usesNeuroSky MindWave mobile 2 to capture brain signalsBetter results of sensitivity precision and specificity indicatethat our approach beats the current literature )e classi-fication accuracy of the proposed DeepLSTM model on ourproposed EEG dataset is 9694 for the 10-fold partitionscheme and outperforms the results of the DeepLSTMmodel on the ASCERTAIN dataset having classificationaccuracy of 9532

We are currently using a single-channel device and inthe future we will extend it to multichannel devices

Data Availability

)e data are available on request from the correspondingauthor

Conflicts of Interest

)e authors declare that they have no conflicts of interest

References

[1] D P McAdams and B D Olson ldquoPersonality developmentcontinuity and change over the life courserdquo Annual Review ofPsychology vol 61 no 1 pp 517ndash542 2010

[2] O P John S E Hampson and L R Goldberg ldquo)e basic levelin personality-trait hierarchies studies of trait use and ac-cessibility in different contextsrdquo Journal of Personality andSocial Psychology vol 60 no 3 pp 348ndash361 1991

[3] F M Cheung and K Leung ldquoIndigenous personality mea-suresrdquo Journal of Cross-Cultural Psychology vol 29 no 1pp 233ndash248 1998

[4] R Harrington and D A Loffredo ldquoMbti personality type andother factors that relate to preference for online versus face-to-face instructionrdquo e Internet and Higher Educationvol 13 no 1-2 pp 89ndash95 2010

[5] R R McCrae and O P John ldquoAn introduction to the five-factor model and its applicationsrdquo Journal of Personalityvol 60 no 2 pp 175ndash215 1992

[6] S K Bhatti A Muneer M I Lali M Gull and S M U DinldquoPersonality analysis of the USA public using twitter profilepicturesrdquo in Proceedings of the 2017 International Conferenceon Information and Communication Technologies (ICICT)pp 165ndash172 Karachi Pakistan December 2017

[7] Y Gvili O Kol and S Levy ldquo)e value (s) of information onsocial network sites the role of user personality traitsrdquo Eu-ropean Review of Applied Psychology vol 70 Article ID100511 2019

[8] A Eftekhar C Fullwood and N Morris ldquoCapturing per-sonality from facebook photos and photo-related activitieshow much exposure do you needrdquo Computers in HumanBehavior vol 37 pp 162ndash170 2014

[9] U Oberst V Renau A Chamarro and X Carbonell ldquoGenderstereotypes in facebook profiles are women more femaleonlinerdquo Computers in Human Behavior vol 60 pp 559ndash5642016

[10] Y-C J Wu W-H Chang and C-H Yuan ldquoDo Facebookprofile pictures reflect userrsquos personalityrdquo Computers inHuman Behavior vol 51 pp 880ndash889 2015

[11] G Stemmler and J Wacker ldquoPersonality emotion and in-dividual differences in physiological responsesrdquo BiologicalPsychology vol 84 no 3 pp 541ndash551 2010

[12] A Baumert M Schmitt and M Perugini ldquoTowards an ex-planatory personality psychology integrating personalitystructure personality process and personality developmentrdquoPersonality and Individual Differences vol 147 pp 18ndash272019

[13] S Kreitler ldquoTowards a consensual model in personalitypsychologyrdquo Personality and Individual Differences vol 147pp 156ndash165 2019

[14] C Montag and J D Elhai ldquoA new agenda for personalitypsychology in the digital agerdquo Personality and IndividualDifferences vol 147 pp 128ndash134 2019

[15] R Akhtar D Winsborough U Ort A Johnson andT Chamorro-Premuzic ldquoDetecting the dark side of per-sonality using social media status updatesrdquo Personality andIndividual Differences vol 132 pp 90ndash97 2018

[16] A C E S Lima and L N De Castro ldquoA multi-label semi-supervised classification approach applied to personalityprediction in social mediardquo Neural Networks vol 58pp 122ndash130 2014

[17] A Bhardwaj A Tiwari M V Varma and M R KrishnaldquoClassification of eeg signals using a novel genetic pro-gramming approachrdquo in Proceedings of the CompanionPublication of the 2014 Annual Conference on Genetic andEvolutionary Computation pp 1297ndash1304 Ireland 2014

[18] H Bhardwaj A Sakalle A Bhardwaj and A TiwarildquoClassification of electroencephalogram signal for the de-tection of epilepsy using innovative genetic programmingrdquoExpert Systems vol 36 no 1 Article ID e12338 2019

[19] A Bhardwaj A Tiwari M V Varma and M R Krishna ldquoAnanalysis of integration of hill climbing in crossover andmutation operation for eeg signal classificationrdquo in Pro-ceedings of the 2015 Annual Conference on Genetic andEvolutionary Computation pp 209ndash216 ACM YangonMyanmar August 2015

[20] T Takahashi T Murata T Hamada et al ldquoChanges in eegand autonomic nervous activity during meditation and theirassociation with personality traitsrdquo International Journal ofPsychophysiology vol 55 no 2 pp 199ndash207 2005

Computational Intelligence and Neuroscience 9

[21] H Bhardwaj P Tomar A Sakalle and A Bhardwaj ldquoClas-sification of extraversion and introversion personality traitusing electroencephalogram signalsrdquo Communications inComputer and Information Science vol 1434 pp 31ndash39 2021

[22] A Sakalle P Tomar H Bhardwaj and A BhardwajldquoEmotion recognition using portable eeg devicerdquo in Com-munications in Computer and Information Science pp 17ndash30Springer International Publishing Berlin Germany 2021

[23] A Kumar N Sinha and A Bhardwaj ldquoA novel fitnessfunction in genetic programming for medical data classifi-cationrdquo Journal of Biomedical Informatics vol 112 Article ID103623 2020

[24] A Purohit A Bhardwaj A Tiwari and N S ChoudharildquoRemoving code bloating in crossover operation in geneticprogrammingrdquo in Proceedings of the 2011 InternationalConference on Recent Trends in Information Technology(ICRTIT) pp 1126ndash1130 Chennai 2011

[25] S Fayolle S Droit-Volet and S Gil ldquoEmotion and timeperception effects of film-induced moodrdquo Procedia - Socialand Behavioral Sciences vol 126 pp 251-252 2014

[26] R Subramanian J Wache M K Abadi R L VieriuS Winkler and N Sebe ldquoAscertain emotion and personalityrecognition using commercial sensorsrdquo IEEE Transactions onAffective Computing vol 9 pp 147ndash160 2016

[27] A Caspi B W Roberts and R L Shiner ldquoPersonality de-velopment stability and changerdquo Annual Review of Psy-chology vol 56 no 1 pp 453ndash484 2005

[28] W Zaremba I Sutskever and O Vinyals ldquoRecurrent neuralnetwork regularizationrdquo 2014 httpsarxivorgabs14092329

[29] A Sakalle P Tomar H Bhardwaj D Acharya andA Bhardwaj ldquoA lstm based deep learning network for rec-ognizing emotions using wireless brainwave driven systemrdquoExpert Systems with Applications vol 173 Article ID 1145162021

[30] A M Bhatti M Majid S M Anwar and B Khan ldquoHumanemotion recognition and analysis in response to audio musicusing brain signalsrdquo Computers in Human Behavior vol 65pp 267ndash275 2016

[31] Y Bengio P Simard and P Frasconi ldquoLearning long-termdependencies with gradient descent is difficultrdquo IEEETransactions on Neural Networks vol 5 no 2 pp 157ndash1661994

[32] H B Mann and D R Whitney ldquoOn a test of whether one oftwo random variables is stochastically larger than the otherrdquoeAnnals of Mathematical Statistics vol 18 no 1 pp 50ndash601947

10 Computational Intelligence and Neuroscience

tanh(x) 2

1 + eminus2x

minus 1 (1)

ldquoSoftmaxrdquo is used as an activation function in the lastlayer and 4 outputs are generated representing four per-sonality classes )e key benefit of using the softmax as anactivation function is the range of output probabilitieswhich will be between 0 and 1 It returns each classrsquosprobabilities with the target class having the highestprobability

Softmax xi( 1113857 exp xi( 1113857

1113936jexp xj1113872 1113873 (2)

LSTM cells and dropout layers are utilised to discoverthe role of EEG signals )e overfitting of these systems wasminimized by restricting unit coadapting in the dropoutlayer of our DeepLSTM architectures )e dense layer theloss function for these network architectures is categoricalcross-entropy and the batch size is 40 )e adaptive momentestimation optimizer (Adam) is used for a learning rate of0001 )e normalization is applied to the dataset inputfeatures with the MinMaxScaler function after loading thedataset )is function normalizes each feature because ofwhich each feature contributes in a maintained manner Itdecreases the internal covariate transition resulting in achange in network activation distribution due to shifts innetwork parameters during training )e normalization ofthe proposed network enhances training reducing thechange in the internal covariance It also helped improve theoptimization phase by stopping weights from burstingaround the entire site by limiting them to a specific set Anundesired advantage of normalization is that it often allowsthe mechanism to regularize somewhat In the parameterspecified by Table 2 the proposed DeepLSTM network isinitialized [29] We test the output of our proposedDeepLSTMmodel which classifies EEG signals as an outputvalue into five personality groups for 500 epochs with a

batch size of 40 )e DeepLSTM model was evaluated usingthe suggested EEG dataset as well as the publicly availableASCERTAIN EEG dataset

)e proposed architectures and parameters were chosenbased on our own experiments with nearby architectures (interms of layers and nodes) In terms of accuracy the pro-posed DeepLSTM architecture outperforms their nearbyarchitecture

5 Experimental Results

)e results of the DeepLSTM model for classifying the EEGsignals and to check our systemrsquos efficacy are presented next)e computer environment is composed of 34GHz devotedto the 32GB RAM-based Python (36) to incorporateDeepLSTM cell architecture and other states of the art ieANN KNN LibSVM and HGP)e parameter values of theANN KNN LibSVM and HGP are the same as in [19 30]respectively )e parameter values taken for the imple-mentation of the DeepLSTM model are given in Table 2

)e dataset is typically split into two distinct sets ietraining sets and test sets A general review of our method iscarried out in this research of personality trait classificationusing EEG signals We have separated the dataset intodifferent training and testing partitions to equate them withexisting literature)e performance assessment is conductedusing a 50ndash50 60ndash40 70ndash30 and 10-fold partition scheme

512 Memory Unit

256 Memory Unit

128 Memory Unit

64 Memory Unit

Input Features

LSTM Layer 1

LSTM Layer 2

LSTM Layer 3

Tanh

SoMax4 Output

DROPOUT LAYER

DROPOUT LAYER

DROPOUT LAYER

DeepLSTM

F1 F2 F3 F10

Figure 4 DeepLSTM cells network architecture

Table 2 DeepLSTM model formation parameter values

Parameter ValueOptimizer AdamRate of learning 0001Rate of dropout 02Loss function Categorical cross-entropyMetrics used AccuracySize of batch 40

Computational Intelligence and Neuroscience 5

In 50ndash50 60ndash40 and 70ndash30 training-testing partition50 60 and 70 respectively data is used for trainingand 50 40 and 30 respectively of the data is used fortesting )e complete dataset is partitioned into approxi-mately ten equal size blocks in a 10-fold cross-validationscheme 90 of the dataset ie nine blocks becomes ourtraining data and 10 of the dataset ie one block becomesour testing data)is process is repeated ten times with eachtime a different data block being used for testing Also ourproposed modelrsquos sensitivity precision and specificity valuefor the 50ndash50 60ndash40 70ndash30 and 10-fold partition schemesare calculated

51 DeepLSTM Architecture Evaluation )is study uses adeep learning algorithm to distinguish personality traitsfrom EEG signals In practice the DeepLSTM model out-performs traditional machine learning algorithms because ithas the capability of remembering the long-term depen-dence of sequential data in time increasing the likelihood ofcorrectness in a short period of time [31]

Table 3 represents the classification accuracy comparisonfor personality prediction DeepLSTM model on the AS-CERTAIN and the proposed EEG datasets For the AS-CERTAIN and the proposed EEG datasets the proposedDeepLSTM model maximum average and minimumclassification accuracy for 50ndash50 60ndash40 70ndash30 and 10-foldcross-validation partition scheme is calculated

)e maximum classification accuracy of the proposedDeepLSTM model for 50ndash50 60ndash40 70ndash30 and 10-foldcross-validation partition scheme on the ASCERTAIN EEGdataset is 8248 8814 9286 and 9532 respectively

)e maximum classification accuracy of the proposedDeepLSTM model for 50ndash50 60ndash40 70ndash30 and 10-foldcross-validation partition scheme on the proposed EEGdataset is 8456 9152 9482 and 9694 respectivelyFrom the results it can be seen that the DeepLSTM modelperforms better in terms of performance on the ASCER-TAIN and the proposed EEG datasets and the classificationaccuracy of the DeepLSTMmodel is higher on our proposedEEG dataset than the ASCERTAIN dataset

6 Discussion

)is section discusses how the proposed deep learning-basedDeepLSTM model works compared to conventional ma-chine learning algorithms

A comparison with standard conventional classificationalgorithms is carried out using the same collection of fea-tures as used in DeepLSTM-based methodology to show theadvantages of incorporating deep learning into the classi-fication of personality traits

)e KNN ANN LibSVM and HGP are the other state-of-the-art approaches used for comparison )e classifica-tion accuracy comparison of personality traits is containedin Table 4 It contains the maximum average and minimumaccuracy for the 50ndash50 60ndash40 70ndash30 and 10-fold partitionschemes )e parameters and settings for these variableshave all been implemented using the same technique toensure that the findings and comparisons offered are un-ambiguous and consistent

)e proposed deep learning approach has a greaterimpact than traditional machine learning algorithms )eDeepLSTM classification improved dramatically in clas-sification accuracy as per the results Besides the rise inclassification accuracy the DeepLSTM classifier can alsoretain specificity greater than 9286 on the ASCERTAINdataset and 9384 on the proposed EEG dataset resultingin very low false prediction rates )e sensitivity value ofthe DeepLSTM model for the ASCERTAIN dataset is9472 and the proposed EEG dataset is 9586 and ishigh in the other state-of-the-art methods which showsthat the DeepLSTM model correctly classifies the minorityclass samples )e precision value of the DeepLSTMmodelfor the ASCERTAIN dataset is 9348 and the proposedEEG dataset is 9444 and is high in the other state-of-the-art methods )e F1 score value of the DeepLSTM modelfor the ASCERTAIN dataset is 9368 and the proposedEEG dataset is 9496 and is high in the other state-of-the-art methods Table 5 shows the relation of sensitivityprecision specificity and F1 score values of DeepLSTM for50ndash50 60ndash40 70ndash30 and 10-fold data partitioningscheme

Table 6 shows the statistical result disparity is illustratedby the two-tailed MannndashWhitney test [32] )e Man-nndashWhitney test is used to compute the p value relation inclassification accuracy )e outcomes do not change sig-nificantly if the p value is greater than 005 and it is highly

Table 3 Classification accuracy comparison for personality pre-diction of DeepLSTM model on the ASCERTAIN and proposedEEG datasets

Dataset Method Validationtechnique

AccuracyMax Avg Min

ASCERTAIN DeepLSTMclassifier

50ndash50 8248 8036 774260ndash40 8814 8563 814670ndash30 9286 8968 864610-fold 9532 9416 9198

Proposeddataset

DeepLSTMclassifier

50ndash50 8456 8244 796260ndash40 9152 8762 848670ndash30 9482 9168 887210-fold 9694 9588 9394

Optimal values are represented in bold

6 Computational Intelligence and Neuroscience

Table 5 Comparison of sensitivity precision and specificity of DeepLSTM model for various partition schemes

Dataset Validation techniqueSensitivity () Precision () Specificity () F1 score ()Meanplusmn Std Meanplusmn Std Meanplusmn Std Meanplusmn Std

ASCERTAIN

50ndash50 8164 plusmn308 8053 plusmn312 7842 plusmn284 8056 plusmn23660ndash40 8776 plusmn314 8682 plusmn324 8594 plusmn268 8644 plusmn24470ndash30 9149 plusmn316 9056 plusmn342 8967 plusmn236 9040 plusmn34210-fold 9472 plusmn 316 9348 plusmn 312 9286 plusmn 298 9368 plusmn 284

Proposed dataset

50ndash50 8346 plusmn314 8275 plusmn315 8114 plusmn324 8246 plusmn31860ndash40 9074 plusmn324 8925 plusmn316 8894 plusmn318 8946 plusmn33270ndash30 9354 plusmn314 9285 plusmn325 9194 plusmn342 9228 plusmn32410-fold 9586 plusmn 318 9444 plusmn 314 9384 plusmn 312 9496 plusmn 316

Optimal values are represented in bold

Table 4 Classification accuracy comparison for personality prediction of DeepLSTM model with other state-of-the-art algorithms on theASCERTAIN and the proposed EEG dataset

Dataset Method Validation techniqueAccuracy

Max Avg Min

ASCERTAIN

ANN 50ndash50 7084 6724 6448KNN 50ndash50 6764 6426 6223

LIBSVM 50ndash50 7728 7384 7186HGP 50ndash50 7852 7538 7268

DeepLSTM classifier 50ndash50 8248 8036 7742

ASCERTAIN

ANN 60ndash40 7434 6986 6774KNN 60ndash40 7038 6816 6568

LIBSVM 60ndash40 7986 7728 7546HGP 60ndash40 8127 7873 7608

DeepLSTM classifier 60ndash40 8814 8563 8146

ASCERTAIN

ANN 70ndash30 7518 7316 6994KNN 70ndash30 7282 7084 6862

LIBSVM 70ndash30 8326 8162 7986HGP 70ndash30 8664 8338 8074

DeepLSTM classifier 70ndash30 9286 8968 8646

ASCERTAIN

ANN 10-fold 7882 7464 7246KNN 10-fold 7436 7237 7025

LIBSVM 10-fold 8482 8242 8028HGP 10-fold 8612 8386 8184

DeepLSTM classifier 10-fold 9532 9416 9198

Proposed dataset

ANN 50ndash50 7284 6936 6648KNN 50ndash50 6932 6692 6386

LIBSVM 50ndash50 7974 7664 7386HGP 50ndash50 8036 7783 7489

DeepLSTM classifier 50ndash50 8456 8244 7962

Proposed dataset

ANN 60ndash40 7616 7328 7012KNN 60ndash40 7264 7062 6854

LIBSVM 60ndash40 8126 7958 7782HGP 60ndash40 8328 8094 7863

DeepLSTM classifier 60ndash40 9152 8762 8486

proposed dataset

ANN 70ndash30 7862 7394 7128KNN 70ndash30 7468 7236 7082

LIBSVM 70ndash30 8564 8370 8158HGP 70ndash30 8769 8474 8284

DeepLSTM classifier 70ndash30 9482 9168 8872

Proposed dataset

ANN 10-fold 8024 7654 7498KNN 10-fold 7682 7464 7228

LIBSVM 10-fold 8804 8516 8326HGP 10-fold 9032 8693 8478

DeepLSTM classifier 10-fold 9694 9588 9394Optimal values are represented in bold

Computational Intelligence and Neuroscience 7

Tabl

e6

pvaluecomparisonforDeepL

STM

usingMannndash

Whitney

test

Training

-testin

gANN

KNN

LibSVM

HGP

Partition

pvalue

Sign

ificance

p-value

Sign

ificance

pvalue

Sign

ificance

pvalue

Sign

ificance

DeepL

STM

50ndash5

02681x

10minus11

Highly

Sign

ificant

1450x

10minus11

Highly

Sign

ificant

19543

10minus11

Highly

Sign

ificant

18563

10minus11

Highly

Sign

ificant

60ndash4

02634x

10minus11

Highly

Sign

ificant

1350x

10minus11

Highly

Sign

ificant

18703

10minus11

Highly

Sign

ificant

18363

10minus11

Highly

Sign

ificant

70ndash3

02576x

10minus11

Highly

Sign

ificant

1270x

10minus11

Highly

Sign

ificant

18423

10minus11

Highly

Sign

ificant

17946

10minus11

Highly

Sign

ificant

10-fold

2486x

10minus11

Highly

Sign

ificant

1235x

10minus11

Highly

Sign

ificant

17856

10minus11

Highly

Sign

ificant

17637

10minus11

Highly

Sign

ificant

Optim

alvalues

arerepresentedin

bold

8 Computational Intelligence and Neuroscience

significant if the p value is less than 0001 It is evident fromthe interventions in Table 6 that the solution provided by ourproposed DeepLSTM model is statistically different fromANN KNN LibSVM andHGP for the 50ndash50 60ndash40 70ndash30and 10-fold data partitioning scheme When the p values arecontrasted with DeepLSTM for these classifiers there is asignificant variation in outcomes )e evaluation resultssuggest that the proposed DeepLSTM-based deep learningmodel for classifying personality traits provides accurateclassification results

7 Conclusion

During this study we propose EEG signals-based personalityprediction system using DeepLSTM-based deep learningmodel

A new EEG dataset was also created using 40 film clips ofHindi and English languages )e proposed DeepLSTMmodel was also applied to the publicly available EEG datasetknown as ASCERTAIN Multiple experiments have beencarried out to validate our results which are helpful tocompare our DeepLSTMmodel with existing methods Fiftyparticipants were involved and saw a few movie clips tar-geting eight different personality traits )is method usesNeuroSky MindWave mobile 2 to capture brain signalsBetter results of sensitivity precision and specificity indicatethat our approach beats the current literature )e classi-fication accuracy of the proposed DeepLSTM model on ourproposed EEG dataset is 9694 for the 10-fold partitionscheme and outperforms the results of the DeepLSTMmodel on the ASCERTAIN dataset having classificationaccuracy of 9532

We are currently using a single-channel device and inthe future we will extend it to multichannel devices

Data Availability

)e data are available on request from the correspondingauthor

Conflicts of Interest

)e authors declare that they have no conflicts of interest

References

[1] D P McAdams and B D Olson ldquoPersonality developmentcontinuity and change over the life courserdquo Annual Review ofPsychology vol 61 no 1 pp 517ndash542 2010

[2] O P John S E Hampson and L R Goldberg ldquo)e basic levelin personality-trait hierarchies studies of trait use and ac-cessibility in different contextsrdquo Journal of Personality andSocial Psychology vol 60 no 3 pp 348ndash361 1991

[3] F M Cheung and K Leung ldquoIndigenous personality mea-suresrdquo Journal of Cross-Cultural Psychology vol 29 no 1pp 233ndash248 1998

[4] R Harrington and D A Loffredo ldquoMbti personality type andother factors that relate to preference for online versus face-to-face instructionrdquo e Internet and Higher Educationvol 13 no 1-2 pp 89ndash95 2010

[5] R R McCrae and O P John ldquoAn introduction to the five-factor model and its applicationsrdquo Journal of Personalityvol 60 no 2 pp 175ndash215 1992

[6] S K Bhatti A Muneer M I Lali M Gull and S M U DinldquoPersonality analysis of the USA public using twitter profilepicturesrdquo in Proceedings of the 2017 International Conferenceon Information and Communication Technologies (ICICT)pp 165ndash172 Karachi Pakistan December 2017

[7] Y Gvili O Kol and S Levy ldquo)e value (s) of information onsocial network sites the role of user personality traitsrdquo Eu-ropean Review of Applied Psychology vol 70 Article ID100511 2019

[8] A Eftekhar C Fullwood and N Morris ldquoCapturing per-sonality from facebook photos and photo-related activitieshow much exposure do you needrdquo Computers in HumanBehavior vol 37 pp 162ndash170 2014

[9] U Oberst V Renau A Chamarro and X Carbonell ldquoGenderstereotypes in facebook profiles are women more femaleonlinerdquo Computers in Human Behavior vol 60 pp 559ndash5642016

[10] Y-C J Wu W-H Chang and C-H Yuan ldquoDo Facebookprofile pictures reflect userrsquos personalityrdquo Computers inHuman Behavior vol 51 pp 880ndash889 2015

[11] G Stemmler and J Wacker ldquoPersonality emotion and in-dividual differences in physiological responsesrdquo BiologicalPsychology vol 84 no 3 pp 541ndash551 2010

[12] A Baumert M Schmitt and M Perugini ldquoTowards an ex-planatory personality psychology integrating personalitystructure personality process and personality developmentrdquoPersonality and Individual Differences vol 147 pp 18ndash272019

[13] S Kreitler ldquoTowards a consensual model in personalitypsychologyrdquo Personality and Individual Differences vol 147pp 156ndash165 2019

[14] C Montag and J D Elhai ldquoA new agenda for personalitypsychology in the digital agerdquo Personality and IndividualDifferences vol 147 pp 128ndash134 2019

[15] R Akhtar D Winsborough U Ort A Johnson andT Chamorro-Premuzic ldquoDetecting the dark side of per-sonality using social media status updatesrdquo Personality andIndividual Differences vol 132 pp 90ndash97 2018

[16] A C E S Lima and L N De Castro ldquoA multi-label semi-supervised classification approach applied to personalityprediction in social mediardquo Neural Networks vol 58pp 122ndash130 2014

[17] A Bhardwaj A Tiwari M V Varma and M R KrishnaldquoClassification of eeg signals using a novel genetic pro-gramming approachrdquo in Proceedings of the CompanionPublication of the 2014 Annual Conference on Genetic andEvolutionary Computation pp 1297ndash1304 Ireland 2014

[18] H Bhardwaj A Sakalle A Bhardwaj and A TiwarildquoClassification of electroencephalogram signal for the de-tection of epilepsy using innovative genetic programmingrdquoExpert Systems vol 36 no 1 Article ID e12338 2019

[19] A Bhardwaj A Tiwari M V Varma and M R Krishna ldquoAnanalysis of integration of hill climbing in crossover andmutation operation for eeg signal classificationrdquo in Pro-ceedings of the 2015 Annual Conference on Genetic andEvolutionary Computation pp 209ndash216 ACM YangonMyanmar August 2015

[20] T Takahashi T Murata T Hamada et al ldquoChanges in eegand autonomic nervous activity during meditation and theirassociation with personality traitsrdquo International Journal ofPsychophysiology vol 55 no 2 pp 199ndash207 2005

Computational Intelligence and Neuroscience 9

[21] H Bhardwaj P Tomar A Sakalle and A Bhardwaj ldquoClas-sification of extraversion and introversion personality traitusing electroencephalogram signalsrdquo Communications inComputer and Information Science vol 1434 pp 31ndash39 2021

[22] A Sakalle P Tomar H Bhardwaj and A BhardwajldquoEmotion recognition using portable eeg devicerdquo in Com-munications in Computer and Information Science pp 17ndash30Springer International Publishing Berlin Germany 2021

[23] A Kumar N Sinha and A Bhardwaj ldquoA novel fitnessfunction in genetic programming for medical data classifi-cationrdquo Journal of Biomedical Informatics vol 112 Article ID103623 2020

[24] A Purohit A Bhardwaj A Tiwari and N S ChoudharildquoRemoving code bloating in crossover operation in geneticprogrammingrdquo in Proceedings of the 2011 InternationalConference on Recent Trends in Information Technology(ICRTIT) pp 1126ndash1130 Chennai 2011

[25] S Fayolle S Droit-Volet and S Gil ldquoEmotion and timeperception effects of film-induced moodrdquo Procedia - Socialand Behavioral Sciences vol 126 pp 251-252 2014

[26] R Subramanian J Wache M K Abadi R L VieriuS Winkler and N Sebe ldquoAscertain emotion and personalityrecognition using commercial sensorsrdquo IEEE Transactions onAffective Computing vol 9 pp 147ndash160 2016

[27] A Caspi B W Roberts and R L Shiner ldquoPersonality de-velopment stability and changerdquo Annual Review of Psy-chology vol 56 no 1 pp 453ndash484 2005

[28] W Zaremba I Sutskever and O Vinyals ldquoRecurrent neuralnetwork regularizationrdquo 2014 httpsarxivorgabs14092329

[29] A Sakalle P Tomar H Bhardwaj D Acharya andA Bhardwaj ldquoA lstm based deep learning network for rec-ognizing emotions using wireless brainwave driven systemrdquoExpert Systems with Applications vol 173 Article ID 1145162021

[30] A M Bhatti M Majid S M Anwar and B Khan ldquoHumanemotion recognition and analysis in response to audio musicusing brain signalsrdquo Computers in Human Behavior vol 65pp 267ndash275 2016

[31] Y Bengio P Simard and P Frasconi ldquoLearning long-termdependencies with gradient descent is difficultrdquo IEEETransactions on Neural Networks vol 5 no 2 pp 157ndash1661994

[32] H B Mann and D R Whitney ldquoOn a test of whether one oftwo random variables is stochastically larger than the otherrdquoeAnnals of Mathematical Statistics vol 18 no 1 pp 50ndash601947

10 Computational Intelligence and Neuroscience

In 50ndash50 60ndash40 and 70ndash30 training-testing partition50 60 and 70 respectively data is used for trainingand 50 40 and 30 respectively of the data is used fortesting )e complete dataset is partitioned into approxi-mately ten equal size blocks in a 10-fold cross-validationscheme 90 of the dataset ie nine blocks becomes ourtraining data and 10 of the dataset ie one block becomesour testing data)is process is repeated ten times with eachtime a different data block being used for testing Also ourproposed modelrsquos sensitivity precision and specificity valuefor the 50ndash50 60ndash40 70ndash30 and 10-fold partition schemesare calculated

51 DeepLSTM Architecture Evaluation )is study uses adeep learning algorithm to distinguish personality traitsfrom EEG signals In practice the DeepLSTM model out-performs traditional machine learning algorithms because ithas the capability of remembering the long-term depen-dence of sequential data in time increasing the likelihood ofcorrectness in a short period of time [31]

Table 3 represents the classification accuracy comparisonfor personality prediction DeepLSTM model on the AS-CERTAIN and the proposed EEG datasets For the AS-CERTAIN and the proposed EEG datasets the proposedDeepLSTM model maximum average and minimumclassification accuracy for 50ndash50 60ndash40 70ndash30 and 10-foldcross-validation partition scheme is calculated

)e maximum classification accuracy of the proposedDeepLSTM model for 50ndash50 60ndash40 70ndash30 and 10-foldcross-validation partition scheme on the ASCERTAIN EEGdataset is 8248 8814 9286 and 9532 respectively

)e maximum classification accuracy of the proposedDeepLSTM model for 50ndash50 60ndash40 70ndash30 and 10-foldcross-validation partition scheme on the proposed EEGdataset is 8456 9152 9482 and 9694 respectivelyFrom the results it can be seen that the DeepLSTM modelperforms better in terms of performance on the ASCER-TAIN and the proposed EEG datasets and the classificationaccuracy of the DeepLSTMmodel is higher on our proposedEEG dataset than the ASCERTAIN dataset

6 Discussion

)is section discusses how the proposed deep learning-basedDeepLSTM model works compared to conventional ma-chine learning algorithms

A comparison with standard conventional classificationalgorithms is carried out using the same collection of fea-tures as used in DeepLSTM-based methodology to show theadvantages of incorporating deep learning into the classi-fication of personality traits

)e KNN ANN LibSVM and HGP are the other state-of-the-art approaches used for comparison )e classifica-tion accuracy comparison of personality traits is containedin Table 4 It contains the maximum average and minimumaccuracy for the 50ndash50 60ndash40 70ndash30 and 10-fold partitionschemes )e parameters and settings for these variableshave all been implemented using the same technique toensure that the findings and comparisons offered are un-ambiguous and consistent

)e proposed deep learning approach has a greaterimpact than traditional machine learning algorithms )eDeepLSTM classification improved dramatically in clas-sification accuracy as per the results Besides the rise inclassification accuracy the DeepLSTM classifier can alsoretain specificity greater than 9286 on the ASCERTAINdataset and 9384 on the proposed EEG dataset resultingin very low false prediction rates )e sensitivity value ofthe DeepLSTM model for the ASCERTAIN dataset is9472 and the proposed EEG dataset is 9586 and ishigh in the other state-of-the-art methods which showsthat the DeepLSTM model correctly classifies the minorityclass samples )e precision value of the DeepLSTMmodelfor the ASCERTAIN dataset is 9348 and the proposedEEG dataset is 9444 and is high in the other state-of-the-art methods )e F1 score value of the DeepLSTM modelfor the ASCERTAIN dataset is 9368 and the proposedEEG dataset is 9496 and is high in the other state-of-the-art methods Table 5 shows the relation of sensitivityprecision specificity and F1 score values of DeepLSTM for50ndash50 60ndash40 70ndash30 and 10-fold data partitioningscheme

Table 6 shows the statistical result disparity is illustratedby the two-tailed MannndashWhitney test [32] )e Man-nndashWhitney test is used to compute the p value relation inclassification accuracy )e outcomes do not change sig-nificantly if the p value is greater than 005 and it is highly

Table 3 Classification accuracy comparison for personality pre-diction of DeepLSTM model on the ASCERTAIN and proposedEEG datasets

Dataset Method Validationtechnique

AccuracyMax Avg Min

ASCERTAIN DeepLSTMclassifier

50ndash50 8248 8036 774260ndash40 8814 8563 814670ndash30 9286 8968 864610-fold 9532 9416 9198

Proposeddataset

DeepLSTMclassifier

50ndash50 8456 8244 796260ndash40 9152 8762 848670ndash30 9482 9168 887210-fold 9694 9588 9394

Optimal values are represented in bold

6 Computational Intelligence and Neuroscience

Table 5 Comparison of sensitivity precision and specificity of DeepLSTM model for various partition schemes

Dataset Validation techniqueSensitivity () Precision () Specificity () F1 score ()Meanplusmn Std Meanplusmn Std Meanplusmn Std Meanplusmn Std

ASCERTAIN

50ndash50 8164 plusmn308 8053 plusmn312 7842 plusmn284 8056 plusmn23660ndash40 8776 plusmn314 8682 plusmn324 8594 plusmn268 8644 plusmn24470ndash30 9149 plusmn316 9056 plusmn342 8967 plusmn236 9040 plusmn34210-fold 9472 plusmn 316 9348 plusmn 312 9286 plusmn 298 9368 plusmn 284

Proposed dataset

50ndash50 8346 plusmn314 8275 plusmn315 8114 plusmn324 8246 plusmn31860ndash40 9074 plusmn324 8925 plusmn316 8894 plusmn318 8946 plusmn33270ndash30 9354 plusmn314 9285 plusmn325 9194 plusmn342 9228 plusmn32410-fold 9586 plusmn 318 9444 plusmn 314 9384 plusmn 312 9496 plusmn 316

Optimal values are represented in bold

Table 4 Classification accuracy comparison for personality prediction of DeepLSTM model with other state-of-the-art algorithms on theASCERTAIN and the proposed EEG dataset

Dataset Method Validation techniqueAccuracy

Max Avg Min

ASCERTAIN

ANN 50ndash50 7084 6724 6448KNN 50ndash50 6764 6426 6223

LIBSVM 50ndash50 7728 7384 7186HGP 50ndash50 7852 7538 7268

DeepLSTM classifier 50ndash50 8248 8036 7742

ASCERTAIN

ANN 60ndash40 7434 6986 6774KNN 60ndash40 7038 6816 6568

LIBSVM 60ndash40 7986 7728 7546HGP 60ndash40 8127 7873 7608

DeepLSTM classifier 60ndash40 8814 8563 8146

ASCERTAIN

ANN 70ndash30 7518 7316 6994KNN 70ndash30 7282 7084 6862

LIBSVM 70ndash30 8326 8162 7986HGP 70ndash30 8664 8338 8074

DeepLSTM classifier 70ndash30 9286 8968 8646

ASCERTAIN

ANN 10-fold 7882 7464 7246KNN 10-fold 7436 7237 7025

LIBSVM 10-fold 8482 8242 8028HGP 10-fold 8612 8386 8184

DeepLSTM classifier 10-fold 9532 9416 9198

Proposed dataset

ANN 50ndash50 7284 6936 6648KNN 50ndash50 6932 6692 6386

LIBSVM 50ndash50 7974 7664 7386HGP 50ndash50 8036 7783 7489

DeepLSTM classifier 50ndash50 8456 8244 7962

Proposed dataset

ANN 60ndash40 7616 7328 7012KNN 60ndash40 7264 7062 6854

LIBSVM 60ndash40 8126 7958 7782HGP 60ndash40 8328 8094 7863

DeepLSTM classifier 60ndash40 9152 8762 8486

proposed dataset

ANN 70ndash30 7862 7394 7128KNN 70ndash30 7468 7236 7082

LIBSVM 70ndash30 8564 8370 8158HGP 70ndash30 8769 8474 8284

DeepLSTM classifier 70ndash30 9482 9168 8872

Proposed dataset

ANN 10-fold 8024 7654 7498KNN 10-fold 7682 7464 7228

LIBSVM 10-fold 8804 8516 8326HGP 10-fold 9032 8693 8478

DeepLSTM classifier 10-fold 9694 9588 9394Optimal values are represented in bold

Computational Intelligence and Neuroscience 7

Tabl

e6

pvaluecomparisonforDeepL

STM

usingMannndash

Whitney

test

Training

-testin

gANN

KNN

LibSVM

HGP

Partition

pvalue

Sign

ificance

p-value

Sign

ificance

pvalue

Sign

ificance

pvalue

Sign

ificance

DeepL

STM

50ndash5

02681x

10minus11

Highly

Sign

ificant

1450x

10minus11

Highly

Sign

ificant

19543

10minus11

Highly

Sign

ificant

18563

10minus11

Highly

Sign

ificant

60ndash4

02634x

10minus11

Highly

Sign

ificant

1350x

10minus11

Highly

Sign

ificant

18703

10minus11

Highly

Sign

ificant

18363

10minus11

Highly

Sign

ificant

70ndash3

02576x

10minus11

Highly

Sign

ificant

1270x

10minus11

Highly

Sign

ificant

18423

10minus11

Highly

Sign

ificant

17946

10minus11

Highly

Sign

ificant

10-fold

2486x

10minus11

Highly

Sign

ificant

1235x

10minus11

Highly

Sign

ificant

17856

10minus11

Highly

Sign

ificant

17637

10minus11

Highly

Sign

ificant

Optim

alvalues

arerepresentedin

bold

8 Computational Intelligence and Neuroscience

significant if the p value is less than 0001 It is evident fromthe interventions in Table 6 that the solution provided by ourproposed DeepLSTM model is statistically different fromANN KNN LibSVM andHGP for the 50ndash50 60ndash40 70ndash30and 10-fold data partitioning scheme When the p values arecontrasted with DeepLSTM for these classifiers there is asignificant variation in outcomes )e evaluation resultssuggest that the proposed DeepLSTM-based deep learningmodel for classifying personality traits provides accurateclassification results

7 Conclusion

During this study we propose EEG signals-based personalityprediction system using DeepLSTM-based deep learningmodel

A new EEG dataset was also created using 40 film clips ofHindi and English languages )e proposed DeepLSTMmodel was also applied to the publicly available EEG datasetknown as ASCERTAIN Multiple experiments have beencarried out to validate our results which are helpful tocompare our DeepLSTMmodel with existing methods Fiftyparticipants were involved and saw a few movie clips tar-geting eight different personality traits )is method usesNeuroSky MindWave mobile 2 to capture brain signalsBetter results of sensitivity precision and specificity indicatethat our approach beats the current literature )e classi-fication accuracy of the proposed DeepLSTM model on ourproposed EEG dataset is 9694 for the 10-fold partitionscheme and outperforms the results of the DeepLSTMmodel on the ASCERTAIN dataset having classificationaccuracy of 9532

We are currently using a single-channel device and inthe future we will extend it to multichannel devices

Data Availability

)e data are available on request from the correspondingauthor

Conflicts of Interest

)e authors declare that they have no conflicts of interest

References

[1] D P McAdams and B D Olson ldquoPersonality developmentcontinuity and change over the life courserdquo Annual Review ofPsychology vol 61 no 1 pp 517ndash542 2010

[2] O P John S E Hampson and L R Goldberg ldquo)e basic levelin personality-trait hierarchies studies of trait use and ac-cessibility in different contextsrdquo Journal of Personality andSocial Psychology vol 60 no 3 pp 348ndash361 1991

[3] F M Cheung and K Leung ldquoIndigenous personality mea-suresrdquo Journal of Cross-Cultural Psychology vol 29 no 1pp 233ndash248 1998

[4] R Harrington and D A Loffredo ldquoMbti personality type andother factors that relate to preference for online versus face-to-face instructionrdquo e Internet and Higher Educationvol 13 no 1-2 pp 89ndash95 2010

[5] R R McCrae and O P John ldquoAn introduction to the five-factor model and its applicationsrdquo Journal of Personalityvol 60 no 2 pp 175ndash215 1992

[6] S K Bhatti A Muneer M I Lali M Gull and S M U DinldquoPersonality analysis of the USA public using twitter profilepicturesrdquo in Proceedings of the 2017 International Conferenceon Information and Communication Technologies (ICICT)pp 165ndash172 Karachi Pakistan December 2017

[7] Y Gvili O Kol and S Levy ldquo)e value (s) of information onsocial network sites the role of user personality traitsrdquo Eu-ropean Review of Applied Psychology vol 70 Article ID100511 2019

[8] A Eftekhar C Fullwood and N Morris ldquoCapturing per-sonality from facebook photos and photo-related activitieshow much exposure do you needrdquo Computers in HumanBehavior vol 37 pp 162ndash170 2014

[9] U Oberst V Renau A Chamarro and X Carbonell ldquoGenderstereotypes in facebook profiles are women more femaleonlinerdquo Computers in Human Behavior vol 60 pp 559ndash5642016

[10] Y-C J Wu W-H Chang and C-H Yuan ldquoDo Facebookprofile pictures reflect userrsquos personalityrdquo Computers inHuman Behavior vol 51 pp 880ndash889 2015

[11] G Stemmler and J Wacker ldquoPersonality emotion and in-dividual differences in physiological responsesrdquo BiologicalPsychology vol 84 no 3 pp 541ndash551 2010

[12] A Baumert M Schmitt and M Perugini ldquoTowards an ex-planatory personality psychology integrating personalitystructure personality process and personality developmentrdquoPersonality and Individual Differences vol 147 pp 18ndash272019

[13] S Kreitler ldquoTowards a consensual model in personalitypsychologyrdquo Personality and Individual Differences vol 147pp 156ndash165 2019

[14] C Montag and J D Elhai ldquoA new agenda for personalitypsychology in the digital agerdquo Personality and IndividualDifferences vol 147 pp 128ndash134 2019

[15] R Akhtar D Winsborough U Ort A Johnson andT Chamorro-Premuzic ldquoDetecting the dark side of per-sonality using social media status updatesrdquo Personality andIndividual Differences vol 132 pp 90ndash97 2018

[16] A C E S Lima and L N De Castro ldquoA multi-label semi-supervised classification approach applied to personalityprediction in social mediardquo Neural Networks vol 58pp 122ndash130 2014

[17] A Bhardwaj A Tiwari M V Varma and M R KrishnaldquoClassification of eeg signals using a novel genetic pro-gramming approachrdquo in Proceedings of the CompanionPublication of the 2014 Annual Conference on Genetic andEvolutionary Computation pp 1297ndash1304 Ireland 2014

[18] H Bhardwaj A Sakalle A Bhardwaj and A TiwarildquoClassification of electroencephalogram signal for the de-tection of epilepsy using innovative genetic programmingrdquoExpert Systems vol 36 no 1 Article ID e12338 2019

[19] A Bhardwaj A Tiwari M V Varma and M R Krishna ldquoAnanalysis of integration of hill climbing in crossover andmutation operation for eeg signal classificationrdquo in Pro-ceedings of the 2015 Annual Conference on Genetic andEvolutionary Computation pp 209ndash216 ACM YangonMyanmar August 2015

[20] T Takahashi T Murata T Hamada et al ldquoChanges in eegand autonomic nervous activity during meditation and theirassociation with personality traitsrdquo International Journal ofPsychophysiology vol 55 no 2 pp 199ndash207 2005

Computational Intelligence and Neuroscience 9

[21] H Bhardwaj P Tomar A Sakalle and A Bhardwaj ldquoClas-sification of extraversion and introversion personality traitusing electroencephalogram signalsrdquo Communications inComputer and Information Science vol 1434 pp 31ndash39 2021

[22] A Sakalle P Tomar H Bhardwaj and A BhardwajldquoEmotion recognition using portable eeg devicerdquo in Com-munications in Computer and Information Science pp 17ndash30Springer International Publishing Berlin Germany 2021

[23] A Kumar N Sinha and A Bhardwaj ldquoA novel fitnessfunction in genetic programming for medical data classifi-cationrdquo Journal of Biomedical Informatics vol 112 Article ID103623 2020

[24] A Purohit A Bhardwaj A Tiwari and N S ChoudharildquoRemoving code bloating in crossover operation in geneticprogrammingrdquo in Proceedings of the 2011 InternationalConference on Recent Trends in Information Technology(ICRTIT) pp 1126ndash1130 Chennai 2011

[25] S Fayolle S Droit-Volet and S Gil ldquoEmotion and timeperception effects of film-induced moodrdquo Procedia - Socialand Behavioral Sciences vol 126 pp 251-252 2014

[26] R Subramanian J Wache M K Abadi R L VieriuS Winkler and N Sebe ldquoAscertain emotion and personalityrecognition using commercial sensorsrdquo IEEE Transactions onAffective Computing vol 9 pp 147ndash160 2016

[27] A Caspi B W Roberts and R L Shiner ldquoPersonality de-velopment stability and changerdquo Annual Review of Psy-chology vol 56 no 1 pp 453ndash484 2005

[28] W Zaremba I Sutskever and O Vinyals ldquoRecurrent neuralnetwork regularizationrdquo 2014 httpsarxivorgabs14092329

[29] A Sakalle P Tomar H Bhardwaj D Acharya andA Bhardwaj ldquoA lstm based deep learning network for rec-ognizing emotions using wireless brainwave driven systemrdquoExpert Systems with Applications vol 173 Article ID 1145162021

[30] A M Bhatti M Majid S M Anwar and B Khan ldquoHumanemotion recognition and analysis in response to audio musicusing brain signalsrdquo Computers in Human Behavior vol 65pp 267ndash275 2016

[31] Y Bengio P Simard and P Frasconi ldquoLearning long-termdependencies with gradient descent is difficultrdquo IEEETransactions on Neural Networks vol 5 no 2 pp 157ndash1661994

[32] H B Mann and D R Whitney ldquoOn a test of whether one oftwo random variables is stochastically larger than the otherrdquoeAnnals of Mathematical Statistics vol 18 no 1 pp 50ndash601947

10 Computational Intelligence and Neuroscience

Table 5 Comparison of sensitivity precision and specificity of DeepLSTM model for various partition schemes

Dataset Validation techniqueSensitivity () Precision () Specificity () F1 score ()Meanplusmn Std Meanplusmn Std Meanplusmn Std Meanplusmn Std

ASCERTAIN

50ndash50 8164 plusmn308 8053 plusmn312 7842 plusmn284 8056 plusmn23660ndash40 8776 plusmn314 8682 plusmn324 8594 plusmn268 8644 plusmn24470ndash30 9149 plusmn316 9056 plusmn342 8967 plusmn236 9040 plusmn34210-fold 9472 plusmn 316 9348 plusmn 312 9286 plusmn 298 9368 plusmn 284

Proposed dataset

50ndash50 8346 plusmn314 8275 plusmn315 8114 plusmn324 8246 plusmn31860ndash40 9074 plusmn324 8925 plusmn316 8894 plusmn318 8946 plusmn33270ndash30 9354 plusmn314 9285 plusmn325 9194 plusmn342 9228 plusmn32410-fold 9586 plusmn 318 9444 plusmn 314 9384 plusmn 312 9496 plusmn 316

Optimal values are represented in bold

Table 4 Classification accuracy comparison for personality prediction of DeepLSTM model with other state-of-the-art algorithms on theASCERTAIN and the proposed EEG dataset

Dataset Method Validation techniqueAccuracy

Max Avg Min

ASCERTAIN

ANN 50ndash50 7084 6724 6448KNN 50ndash50 6764 6426 6223

LIBSVM 50ndash50 7728 7384 7186HGP 50ndash50 7852 7538 7268

DeepLSTM classifier 50ndash50 8248 8036 7742

ASCERTAIN

ANN 60ndash40 7434 6986 6774KNN 60ndash40 7038 6816 6568

LIBSVM 60ndash40 7986 7728 7546HGP 60ndash40 8127 7873 7608

DeepLSTM classifier 60ndash40 8814 8563 8146

ASCERTAIN

ANN 70ndash30 7518 7316 6994KNN 70ndash30 7282 7084 6862

LIBSVM 70ndash30 8326 8162 7986HGP 70ndash30 8664 8338 8074

DeepLSTM classifier 70ndash30 9286 8968 8646

ASCERTAIN

ANN 10-fold 7882 7464 7246KNN 10-fold 7436 7237 7025

LIBSVM 10-fold 8482 8242 8028HGP 10-fold 8612 8386 8184

DeepLSTM classifier 10-fold 9532 9416 9198

Proposed dataset

ANN 50ndash50 7284 6936 6648KNN 50ndash50 6932 6692 6386

LIBSVM 50ndash50 7974 7664 7386HGP 50ndash50 8036 7783 7489

DeepLSTM classifier 50ndash50 8456 8244 7962

Proposed dataset

ANN 60ndash40 7616 7328 7012KNN 60ndash40 7264 7062 6854

LIBSVM 60ndash40 8126 7958 7782HGP 60ndash40 8328 8094 7863

DeepLSTM classifier 60ndash40 9152 8762 8486

proposed dataset

ANN 70ndash30 7862 7394 7128KNN 70ndash30 7468 7236 7082

LIBSVM 70ndash30 8564 8370 8158HGP 70ndash30 8769 8474 8284

DeepLSTM classifier 70ndash30 9482 9168 8872

Proposed dataset

ANN 10-fold 8024 7654 7498KNN 10-fold 7682 7464 7228

LIBSVM 10-fold 8804 8516 8326HGP 10-fold 9032 8693 8478

DeepLSTM classifier 10-fold 9694 9588 9394Optimal values are represented in bold

Computational Intelligence and Neuroscience 7

Tabl

e6

pvaluecomparisonforDeepL

STM

usingMannndash

Whitney

test

Training

-testin

gANN

KNN

LibSVM

HGP

Partition

pvalue

Sign

ificance

p-value

Sign

ificance

pvalue

Sign

ificance

pvalue

Sign

ificance

DeepL

STM

50ndash5

02681x

10minus11

Highly

Sign

ificant

1450x

10minus11

Highly

Sign

ificant

19543

10minus11

Highly

Sign

ificant

18563

10minus11

Highly

Sign

ificant

60ndash4

02634x

10minus11

Highly

Sign

ificant

1350x

10minus11

Highly

Sign

ificant

18703

10minus11

Highly

Sign

ificant

18363

10minus11

Highly

Sign

ificant

70ndash3

02576x

10minus11

Highly

Sign

ificant

1270x

10minus11

Highly

Sign

ificant

18423

10minus11

Highly

Sign

ificant

17946

10minus11

Highly

Sign

ificant

10-fold

2486x

10minus11

Highly

Sign

ificant

1235x

10minus11

Highly

Sign

ificant

17856

10minus11

Highly

Sign

ificant

17637

10minus11

Highly

Sign

ificant

Optim

alvalues

arerepresentedin

bold

8 Computational Intelligence and Neuroscience

significant if the p value is less than 0001 It is evident fromthe interventions in Table 6 that the solution provided by ourproposed DeepLSTM model is statistically different fromANN KNN LibSVM andHGP for the 50ndash50 60ndash40 70ndash30and 10-fold data partitioning scheme When the p values arecontrasted with DeepLSTM for these classifiers there is asignificant variation in outcomes )e evaluation resultssuggest that the proposed DeepLSTM-based deep learningmodel for classifying personality traits provides accurateclassification results

7 Conclusion

During this study we propose EEG signals-based personalityprediction system using DeepLSTM-based deep learningmodel

A new EEG dataset was also created using 40 film clips ofHindi and English languages )e proposed DeepLSTMmodel was also applied to the publicly available EEG datasetknown as ASCERTAIN Multiple experiments have beencarried out to validate our results which are helpful tocompare our DeepLSTMmodel with existing methods Fiftyparticipants were involved and saw a few movie clips tar-geting eight different personality traits )is method usesNeuroSky MindWave mobile 2 to capture brain signalsBetter results of sensitivity precision and specificity indicatethat our approach beats the current literature )e classi-fication accuracy of the proposed DeepLSTM model on ourproposed EEG dataset is 9694 for the 10-fold partitionscheme and outperforms the results of the DeepLSTMmodel on the ASCERTAIN dataset having classificationaccuracy of 9532

We are currently using a single-channel device and inthe future we will extend it to multichannel devices

Data Availability

)e data are available on request from the correspondingauthor

Conflicts of Interest

)e authors declare that they have no conflicts of interest

References

[1] D P McAdams and B D Olson ldquoPersonality developmentcontinuity and change over the life courserdquo Annual Review ofPsychology vol 61 no 1 pp 517ndash542 2010

[2] O P John S E Hampson and L R Goldberg ldquo)e basic levelin personality-trait hierarchies studies of trait use and ac-cessibility in different contextsrdquo Journal of Personality andSocial Psychology vol 60 no 3 pp 348ndash361 1991

[3] F M Cheung and K Leung ldquoIndigenous personality mea-suresrdquo Journal of Cross-Cultural Psychology vol 29 no 1pp 233ndash248 1998

[4] R Harrington and D A Loffredo ldquoMbti personality type andother factors that relate to preference for online versus face-to-face instructionrdquo e Internet and Higher Educationvol 13 no 1-2 pp 89ndash95 2010

[5] R R McCrae and O P John ldquoAn introduction to the five-factor model and its applicationsrdquo Journal of Personalityvol 60 no 2 pp 175ndash215 1992

[6] S K Bhatti A Muneer M I Lali M Gull and S M U DinldquoPersonality analysis of the USA public using twitter profilepicturesrdquo in Proceedings of the 2017 International Conferenceon Information and Communication Technologies (ICICT)pp 165ndash172 Karachi Pakistan December 2017

[7] Y Gvili O Kol and S Levy ldquo)e value (s) of information onsocial network sites the role of user personality traitsrdquo Eu-ropean Review of Applied Psychology vol 70 Article ID100511 2019

[8] A Eftekhar C Fullwood and N Morris ldquoCapturing per-sonality from facebook photos and photo-related activitieshow much exposure do you needrdquo Computers in HumanBehavior vol 37 pp 162ndash170 2014

[9] U Oberst V Renau A Chamarro and X Carbonell ldquoGenderstereotypes in facebook profiles are women more femaleonlinerdquo Computers in Human Behavior vol 60 pp 559ndash5642016

[10] Y-C J Wu W-H Chang and C-H Yuan ldquoDo Facebookprofile pictures reflect userrsquos personalityrdquo Computers inHuman Behavior vol 51 pp 880ndash889 2015

[11] G Stemmler and J Wacker ldquoPersonality emotion and in-dividual differences in physiological responsesrdquo BiologicalPsychology vol 84 no 3 pp 541ndash551 2010

[12] A Baumert M Schmitt and M Perugini ldquoTowards an ex-planatory personality psychology integrating personalitystructure personality process and personality developmentrdquoPersonality and Individual Differences vol 147 pp 18ndash272019

[13] S Kreitler ldquoTowards a consensual model in personalitypsychologyrdquo Personality and Individual Differences vol 147pp 156ndash165 2019

[14] C Montag and J D Elhai ldquoA new agenda for personalitypsychology in the digital agerdquo Personality and IndividualDifferences vol 147 pp 128ndash134 2019

[15] R Akhtar D Winsborough U Ort A Johnson andT Chamorro-Premuzic ldquoDetecting the dark side of per-sonality using social media status updatesrdquo Personality andIndividual Differences vol 132 pp 90ndash97 2018

[16] A C E S Lima and L N De Castro ldquoA multi-label semi-supervised classification approach applied to personalityprediction in social mediardquo Neural Networks vol 58pp 122ndash130 2014

[17] A Bhardwaj A Tiwari M V Varma and M R KrishnaldquoClassification of eeg signals using a novel genetic pro-gramming approachrdquo in Proceedings of the CompanionPublication of the 2014 Annual Conference on Genetic andEvolutionary Computation pp 1297ndash1304 Ireland 2014

[18] H Bhardwaj A Sakalle A Bhardwaj and A TiwarildquoClassification of electroencephalogram signal for the de-tection of epilepsy using innovative genetic programmingrdquoExpert Systems vol 36 no 1 Article ID e12338 2019

[19] A Bhardwaj A Tiwari M V Varma and M R Krishna ldquoAnanalysis of integration of hill climbing in crossover andmutation operation for eeg signal classificationrdquo in Pro-ceedings of the 2015 Annual Conference on Genetic andEvolutionary Computation pp 209ndash216 ACM YangonMyanmar August 2015

[20] T Takahashi T Murata T Hamada et al ldquoChanges in eegand autonomic nervous activity during meditation and theirassociation with personality traitsrdquo International Journal ofPsychophysiology vol 55 no 2 pp 199ndash207 2005

Computational Intelligence and Neuroscience 9

[21] H Bhardwaj P Tomar A Sakalle and A Bhardwaj ldquoClas-sification of extraversion and introversion personality traitusing electroencephalogram signalsrdquo Communications inComputer and Information Science vol 1434 pp 31ndash39 2021

[22] A Sakalle P Tomar H Bhardwaj and A BhardwajldquoEmotion recognition using portable eeg devicerdquo in Com-munications in Computer and Information Science pp 17ndash30Springer International Publishing Berlin Germany 2021

[23] A Kumar N Sinha and A Bhardwaj ldquoA novel fitnessfunction in genetic programming for medical data classifi-cationrdquo Journal of Biomedical Informatics vol 112 Article ID103623 2020

[24] A Purohit A Bhardwaj A Tiwari and N S ChoudharildquoRemoving code bloating in crossover operation in geneticprogrammingrdquo in Proceedings of the 2011 InternationalConference on Recent Trends in Information Technology(ICRTIT) pp 1126ndash1130 Chennai 2011

[25] S Fayolle S Droit-Volet and S Gil ldquoEmotion and timeperception effects of film-induced moodrdquo Procedia - Socialand Behavioral Sciences vol 126 pp 251-252 2014

[26] R Subramanian J Wache M K Abadi R L VieriuS Winkler and N Sebe ldquoAscertain emotion and personalityrecognition using commercial sensorsrdquo IEEE Transactions onAffective Computing vol 9 pp 147ndash160 2016

[27] A Caspi B W Roberts and R L Shiner ldquoPersonality de-velopment stability and changerdquo Annual Review of Psy-chology vol 56 no 1 pp 453ndash484 2005

[28] W Zaremba I Sutskever and O Vinyals ldquoRecurrent neuralnetwork regularizationrdquo 2014 httpsarxivorgabs14092329

[29] A Sakalle P Tomar H Bhardwaj D Acharya andA Bhardwaj ldquoA lstm based deep learning network for rec-ognizing emotions using wireless brainwave driven systemrdquoExpert Systems with Applications vol 173 Article ID 1145162021

[30] A M Bhatti M Majid S M Anwar and B Khan ldquoHumanemotion recognition and analysis in response to audio musicusing brain signalsrdquo Computers in Human Behavior vol 65pp 267ndash275 2016

[31] Y Bengio P Simard and P Frasconi ldquoLearning long-termdependencies with gradient descent is difficultrdquo IEEETransactions on Neural Networks vol 5 no 2 pp 157ndash1661994

[32] H B Mann and D R Whitney ldquoOn a test of whether one oftwo random variables is stochastically larger than the otherrdquoeAnnals of Mathematical Statistics vol 18 no 1 pp 50ndash601947

10 Computational Intelligence and Neuroscience

Tabl

e6

pvaluecomparisonforDeepL

STM

usingMannndash

Whitney

test

Training

-testin

gANN

KNN

LibSVM

HGP

Partition

pvalue

Sign

ificance

p-value

Sign

ificance

pvalue

Sign

ificance

pvalue

Sign

ificance

DeepL

STM

50ndash5

02681x

10minus11

Highly

Sign

ificant

1450x

10minus11

Highly

Sign

ificant

19543

10minus11

Highly

Sign

ificant

18563

10minus11

Highly

Sign

ificant

60ndash4

02634x

10minus11

Highly

Sign

ificant

1350x

10minus11

Highly

Sign

ificant

18703

10minus11

Highly

Sign

ificant

18363

10minus11

Highly

Sign

ificant

70ndash3

02576x

10minus11

Highly

Sign

ificant

1270x

10minus11

Highly

Sign

ificant

18423

10minus11

Highly

Sign

ificant

17946

10minus11

Highly

Sign

ificant

10-fold

2486x

10minus11

Highly

Sign

ificant

1235x

10minus11

Highly

Sign

ificant

17856

10minus11

Highly

Sign

ificant

17637

10minus11

Highly

Sign

ificant

Optim

alvalues

arerepresentedin

bold

8 Computational Intelligence and Neuroscience

significant if the p value is less than 0001 It is evident fromthe interventions in Table 6 that the solution provided by ourproposed DeepLSTM model is statistically different fromANN KNN LibSVM andHGP for the 50ndash50 60ndash40 70ndash30and 10-fold data partitioning scheme When the p values arecontrasted with DeepLSTM for these classifiers there is asignificant variation in outcomes )e evaluation resultssuggest that the proposed DeepLSTM-based deep learningmodel for classifying personality traits provides accurateclassification results

7 Conclusion

During this study we propose EEG signals-based personalityprediction system using DeepLSTM-based deep learningmodel

A new EEG dataset was also created using 40 film clips ofHindi and English languages )e proposed DeepLSTMmodel was also applied to the publicly available EEG datasetknown as ASCERTAIN Multiple experiments have beencarried out to validate our results which are helpful tocompare our DeepLSTMmodel with existing methods Fiftyparticipants were involved and saw a few movie clips tar-geting eight different personality traits )is method usesNeuroSky MindWave mobile 2 to capture brain signalsBetter results of sensitivity precision and specificity indicatethat our approach beats the current literature )e classi-fication accuracy of the proposed DeepLSTM model on ourproposed EEG dataset is 9694 for the 10-fold partitionscheme and outperforms the results of the DeepLSTMmodel on the ASCERTAIN dataset having classificationaccuracy of 9532

We are currently using a single-channel device and inthe future we will extend it to multichannel devices

Data Availability

)e data are available on request from the correspondingauthor

Conflicts of Interest

)e authors declare that they have no conflicts of interest

References

[1] D P McAdams and B D Olson ldquoPersonality developmentcontinuity and change over the life courserdquo Annual Review ofPsychology vol 61 no 1 pp 517ndash542 2010

[2] O P John S E Hampson and L R Goldberg ldquo)e basic levelin personality-trait hierarchies studies of trait use and ac-cessibility in different contextsrdquo Journal of Personality andSocial Psychology vol 60 no 3 pp 348ndash361 1991

[3] F M Cheung and K Leung ldquoIndigenous personality mea-suresrdquo Journal of Cross-Cultural Psychology vol 29 no 1pp 233ndash248 1998

[4] R Harrington and D A Loffredo ldquoMbti personality type andother factors that relate to preference for online versus face-to-face instructionrdquo e Internet and Higher Educationvol 13 no 1-2 pp 89ndash95 2010

[5] R R McCrae and O P John ldquoAn introduction to the five-factor model and its applicationsrdquo Journal of Personalityvol 60 no 2 pp 175ndash215 1992

[6] S K Bhatti A Muneer M I Lali M Gull and S M U DinldquoPersonality analysis of the USA public using twitter profilepicturesrdquo in Proceedings of the 2017 International Conferenceon Information and Communication Technologies (ICICT)pp 165ndash172 Karachi Pakistan December 2017

[7] Y Gvili O Kol and S Levy ldquo)e value (s) of information onsocial network sites the role of user personality traitsrdquo Eu-ropean Review of Applied Psychology vol 70 Article ID100511 2019

[8] A Eftekhar C Fullwood and N Morris ldquoCapturing per-sonality from facebook photos and photo-related activitieshow much exposure do you needrdquo Computers in HumanBehavior vol 37 pp 162ndash170 2014

[9] U Oberst V Renau A Chamarro and X Carbonell ldquoGenderstereotypes in facebook profiles are women more femaleonlinerdquo Computers in Human Behavior vol 60 pp 559ndash5642016

[10] Y-C J Wu W-H Chang and C-H Yuan ldquoDo Facebookprofile pictures reflect userrsquos personalityrdquo Computers inHuman Behavior vol 51 pp 880ndash889 2015

[11] G Stemmler and J Wacker ldquoPersonality emotion and in-dividual differences in physiological responsesrdquo BiologicalPsychology vol 84 no 3 pp 541ndash551 2010

[12] A Baumert M Schmitt and M Perugini ldquoTowards an ex-planatory personality psychology integrating personalitystructure personality process and personality developmentrdquoPersonality and Individual Differences vol 147 pp 18ndash272019

[13] S Kreitler ldquoTowards a consensual model in personalitypsychologyrdquo Personality and Individual Differences vol 147pp 156ndash165 2019

[14] C Montag and J D Elhai ldquoA new agenda for personalitypsychology in the digital agerdquo Personality and IndividualDifferences vol 147 pp 128ndash134 2019

[15] R Akhtar D Winsborough U Ort A Johnson andT Chamorro-Premuzic ldquoDetecting the dark side of per-sonality using social media status updatesrdquo Personality andIndividual Differences vol 132 pp 90ndash97 2018

[16] A C E S Lima and L N De Castro ldquoA multi-label semi-supervised classification approach applied to personalityprediction in social mediardquo Neural Networks vol 58pp 122ndash130 2014

[17] A Bhardwaj A Tiwari M V Varma and M R KrishnaldquoClassification of eeg signals using a novel genetic pro-gramming approachrdquo in Proceedings of the CompanionPublication of the 2014 Annual Conference on Genetic andEvolutionary Computation pp 1297ndash1304 Ireland 2014

[18] H Bhardwaj A Sakalle A Bhardwaj and A TiwarildquoClassification of electroencephalogram signal for the de-tection of epilepsy using innovative genetic programmingrdquoExpert Systems vol 36 no 1 Article ID e12338 2019

[19] A Bhardwaj A Tiwari M V Varma and M R Krishna ldquoAnanalysis of integration of hill climbing in crossover andmutation operation for eeg signal classificationrdquo in Pro-ceedings of the 2015 Annual Conference on Genetic andEvolutionary Computation pp 209ndash216 ACM YangonMyanmar August 2015

[20] T Takahashi T Murata T Hamada et al ldquoChanges in eegand autonomic nervous activity during meditation and theirassociation with personality traitsrdquo International Journal ofPsychophysiology vol 55 no 2 pp 199ndash207 2005

Computational Intelligence and Neuroscience 9

[21] H Bhardwaj P Tomar A Sakalle and A Bhardwaj ldquoClas-sification of extraversion and introversion personality traitusing electroencephalogram signalsrdquo Communications inComputer and Information Science vol 1434 pp 31ndash39 2021

[22] A Sakalle P Tomar H Bhardwaj and A BhardwajldquoEmotion recognition using portable eeg devicerdquo in Com-munications in Computer and Information Science pp 17ndash30Springer International Publishing Berlin Germany 2021

[23] A Kumar N Sinha and A Bhardwaj ldquoA novel fitnessfunction in genetic programming for medical data classifi-cationrdquo Journal of Biomedical Informatics vol 112 Article ID103623 2020

[24] A Purohit A Bhardwaj A Tiwari and N S ChoudharildquoRemoving code bloating in crossover operation in geneticprogrammingrdquo in Proceedings of the 2011 InternationalConference on Recent Trends in Information Technology(ICRTIT) pp 1126ndash1130 Chennai 2011

[25] S Fayolle S Droit-Volet and S Gil ldquoEmotion and timeperception effects of film-induced moodrdquo Procedia - Socialand Behavioral Sciences vol 126 pp 251-252 2014

[26] R Subramanian J Wache M K Abadi R L VieriuS Winkler and N Sebe ldquoAscertain emotion and personalityrecognition using commercial sensorsrdquo IEEE Transactions onAffective Computing vol 9 pp 147ndash160 2016

[27] A Caspi B W Roberts and R L Shiner ldquoPersonality de-velopment stability and changerdquo Annual Review of Psy-chology vol 56 no 1 pp 453ndash484 2005

[28] W Zaremba I Sutskever and O Vinyals ldquoRecurrent neuralnetwork regularizationrdquo 2014 httpsarxivorgabs14092329

[29] A Sakalle P Tomar H Bhardwaj D Acharya andA Bhardwaj ldquoA lstm based deep learning network for rec-ognizing emotions using wireless brainwave driven systemrdquoExpert Systems with Applications vol 173 Article ID 1145162021

[30] A M Bhatti M Majid S M Anwar and B Khan ldquoHumanemotion recognition and analysis in response to audio musicusing brain signalsrdquo Computers in Human Behavior vol 65pp 267ndash275 2016

[31] Y Bengio P Simard and P Frasconi ldquoLearning long-termdependencies with gradient descent is difficultrdquo IEEETransactions on Neural Networks vol 5 no 2 pp 157ndash1661994

[32] H B Mann and D R Whitney ldquoOn a test of whether one oftwo random variables is stochastically larger than the otherrdquoeAnnals of Mathematical Statistics vol 18 no 1 pp 50ndash601947

10 Computational Intelligence and Neuroscience

significant if the p value is less than 0001 It is evident fromthe interventions in Table 6 that the solution provided by ourproposed DeepLSTM model is statistically different fromANN KNN LibSVM andHGP for the 50ndash50 60ndash40 70ndash30and 10-fold data partitioning scheme When the p values arecontrasted with DeepLSTM for these classifiers there is asignificant variation in outcomes )e evaluation resultssuggest that the proposed DeepLSTM-based deep learningmodel for classifying personality traits provides accurateclassification results

7 Conclusion

During this study we propose EEG signals-based personalityprediction system using DeepLSTM-based deep learningmodel

A new EEG dataset was also created using 40 film clips ofHindi and English languages )e proposed DeepLSTMmodel was also applied to the publicly available EEG datasetknown as ASCERTAIN Multiple experiments have beencarried out to validate our results which are helpful tocompare our DeepLSTMmodel with existing methods Fiftyparticipants were involved and saw a few movie clips tar-geting eight different personality traits )is method usesNeuroSky MindWave mobile 2 to capture brain signalsBetter results of sensitivity precision and specificity indicatethat our approach beats the current literature )e classi-fication accuracy of the proposed DeepLSTM model on ourproposed EEG dataset is 9694 for the 10-fold partitionscheme and outperforms the results of the DeepLSTMmodel on the ASCERTAIN dataset having classificationaccuracy of 9532

We are currently using a single-channel device and inthe future we will extend it to multichannel devices

Data Availability

)e data are available on request from the correspondingauthor

Conflicts of Interest

)e authors declare that they have no conflicts of interest

References

[1] D P McAdams and B D Olson ldquoPersonality developmentcontinuity and change over the life courserdquo Annual Review ofPsychology vol 61 no 1 pp 517ndash542 2010

[2] O P John S E Hampson and L R Goldberg ldquo)e basic levelin personality-trait hierarchies studies of trait use and ac-cessibility in different contextsrdquo Journal of Personality andSocial Psychology vol 60 no 3 pp 348ndash361 1991

[3] F M Cheung and K Leung ldquoIndigenous personality mea-suresrdquo Journal of Cross-Cultural Psychology vol 29 no 1pp 233ndash248 1998

[4] R Harrington and D A Loffredo ldquoMbti personality type andother factors that relate to preference for online versus face-to-face instructionrdquo e Internet and Higher Educationvol 13 no 1-2 pp 89ndash95 2010

[5] R R McCrae and O P John ldquoAn introduction to the five-factor model and its applicationsrdquo Journal of Personalityvol 60 no 2 pp 175ndash215 1992

[6] S K Bhatti A Muneer M I Lali M Gull and S M U DinldquoPersonality analysis of the USA public using twitter profilepicturesrdquo in Proceedings of the 2017 International Conferenceon Information and Communication Technologies (ICICT)pp 165ndash172 Karachi Pakistan December 2017

[7] Y Gvili O Kol and S Levy ldquo)e value (s) of information onsocial network sites the role of user personality traitsrdquo Eu-ropean Review of Applied Psychology vol 70 Article ID100511 2019

[8] A Eftekhar C Fullwood and N Morris ldquoCapturing per-sonality from facebook photos and photo-related activitieshow much exposure do you needrdquo Computers in HumanBehavior vol 37 pp 162ndash170 2014

[9] U Oberst V Renau A Chamarro and X Carbonell ldquoGenderstereotypes in facebook profiles are women more femaleonlinerdquo Computers in Human Behavior vol 60 pp 559ndash5642016

[10] Y-C J Wu W-H Chang and C-H Yuan ldquoDo Facebookprofile pictures reflect userrsquos personalityrdquo Computers inHuman Behavior vol 51 pp 880ndash889 2015

[11] G Stemmler and J Wacker ldquoPersonality emotion and in-dividual differences in physiological responsesrdquo BiologicalPsychology vol 84 no 3 pp 541ndash551 2010

[12] A Baumert M Schmitt and M Perugini ldquoTowards an ex-planatory personality psychology integrating personalitystructure personality process and personality developmentrdquoPersonality and Individual Differences vol 147 pp 18ndash272019

[13] S Kreitler ldquoTowards a consensual model in personalitypsychologyrdquo Personality and Individual Differences vol 147pp 156ndash165 2019

[14] C Montag and J D Elhai ldquoA new agenda for personalitypsychology in the digital agerdquo Personality and IndividualDifferences vol 147 pp 128ndash134 2019

[15] R Akhtar D Winsborough U Ort A Johnson andT Chamorro-Premuzic ldquoDetecting the dark side of per-sonality using social media status updatesrdquo Personality andIndividual Differences vol 132 pp 90ndash97 2018

[16] A C E S Lima and L N De Castro ldquoA multi-label semi-supervised classification approach applied to personalityprediction in social mediardquo Neural Networks vol 58pp 122ndash130 2014

[17] A Bhardwaj A Tiwari M V Varma and M R KrishnaldquoClassification of eeg signals using a novel genetic pro-gramming approachrdquo in Proceedings of the CompanionPublication of the 2014 Annual Conference on Genetic andEvolutionary Computation pp 1297ndash1304 Ireland 2014

[18] H Bhardwaj A Sakalle A Bhardwaj and A TiwarildquoClassification of electroencephalogram signal for the de-tection of epilepsy using innovative genetic programmingrdquoExpert Systems vol 36 no 1 Article ID e12338 2019

[19] A Bhardwaj A Tiwari M V Varma and M R Krishna ldquoAnanalysis of integration of hill climbing in crossover andmutation operation for eeg signal classificationrdquo in Pro-ceedings of the 2015 Annual Conference on Genetic andEvolutionary Computation pp 209ndash216 ACM YangonMyanmar August 2015

[20] T Takahashi T Murata T Hamada et al ldquoChanges in eegand autonomic nervous activity during meditation and theirassociation with personality traitsrdquo International Journal ofPsychophysiology vol 55 no 2 pp 199ndash207 2005

Computational Intelligence and Neuroscience 9

[21] H Bhardwaj P Tomar A Sakalle and A Bhardwaj ldquoClas-sification of extraversion and introversion personality traitusing electroencephalogram signalsrdquo Communications inComputer and Information Science vol 1434 pp 31ndash39 2021

[22] A Sakalle P Tomar H Bhardwaj and A BhardwajldquoEmotion recognition using portable eeg devicerdquo in Com-munications in Computer and Information Science pp 17ndash30Springer International Publishing Berlin Germany 2021

[23] A Kumar N Sinha and A Bhardwaj ldquoA novel fitnessfunction in genetic programming for medical data classifi-cationrdquo Journal of Biomedical Informatics vol 112 Article ID103623 2020

[24] A Purohit A Bhardwaj A Tiwari and N S ChoudharildquoRemoving code bloating in crossover operation in geneticprogrammingrdquo in Proceedings of the 2011 InternationalConference on Recent Trends in Information Technology(ICRTIT) pp 1126ndash1130 Chennai 2011

[25] S Fayolle S Droit-Volet and S Gil ldquoEmotion and timeperception effects of film-induced moodrdquo Procedia - Socialand Behavioral Sciences vol 126 pp 251-252 2014

[26] R Subramanian J Wache M K Abadi R L VieriuS Winkler and N Sebe ldquoAscertain emotion and personalityrecognition using commercial sensorsrdquo IEEE Transactions onAffective Computing vol 9 pp 147ndash160 2016

[27] A Caspi B W Roberts and R L Shiner ldquoPersonality de-velopment stability and changerdquo Annual Review of Psy-chology vol 56 no 1 pp 453ndash484 2005

[28] W Zaremba I Sutskever and O Vinyals ldquoRecurrent neuralnetwork regularizationrdquo 2014 httpsarxivorgabs14092329

[29] A Sakalle P Tomar H Bhardwaj D Acharya andA Bhardwaj ldquoA lstm based deep learning network for rec-ognizing emotions using wireless brainwave driven systemrdquoExpert Systems with Applications vol 173 Article ID 1145162021

[30] A M Bhatti M Majid S M Anwar and B Khan ldquoHumanemotion recognition and analysis in response to audio musicusing brain signalsrdquo Computers in Human Behavior vol 65pp 267ndash275 2016

[31] Y Bengio P Simard and P Frasconi ldquoLearning long-termdependencies with gradient descent is difficultrdquo IEEETransactions on Neural Networks vol 5 no 2 pp 157ndash1661994

[32] H B Mann and D R Whitney ldquoOn a test of whether one oftwo random variables is stochastically larger than the otherrdquoeAnnals of Mathematical Statistics vol 18 no 1 pp 50ndash601947

10 Computational Intelligence and Neuroscience

[21] H Bhardwaj P Tomar A Sakalle and A Bhardwaj ldquoClas-sification of extraversion and introversion personality traitusing electroencephalogram signalsrdquo Communications inComputer and Information Science vol 1434 pp 31ndash39 2021

[22] A Sakalle P Tomar H Bhardwaj and A BhardwajldquoEmotion recognition using portable eeg devicerdquo in Com-munications in Computer and Information Science pp 17ndash30Springer International Publishing Berlin Germany 2021

[23] A Kumar N Sinha and A Bhardwaj ldquoA novel fitnessfunction in genetic programming for medical data classifi-cationrdquo Journal of Biomedical Informatics vol 112 Article ID103623 2020

[24] A Purohit A Bhardwaj A Tiwari and N S ChoudharildquoRemoving code bloating in crossover operation in geneticprogrammingrdquo in Proceedings of the 2011 InternationalConference on Recent Trends in Information Technology(ICRTIT) pp 1126ndash1130 Chennai 2011

[25] S Fayolle S Droit-Volet and S Gil ldquoEmotion and timeperception effects of film-induced moodrdquo Procedia - Socialand Behavioral Sciences vol 126 pp 251-252 2014

[26] R Subramanian J Wache M K Abadi R L VieriuS Winkler and N Sebe ldquoAscertain emotion and personalityrecognition using commercial sensorsrdquo IEEE Transactions onAffective Computing vol 9 pp 147ndash160 2016

[27] A Caspi B W Roberts and R L Shiner ldquoPersonality de-velopment stability and changerdquo Annual Review of Psy-chology vol 56 no 1 pp 453ndash484 2005

[28] W Zaremba I Sutskever and O Vinyals ldquoRecurrent neuralnetwork regularizationrdquo 2014 httpsarxivorgabs14092329

[29] A Sakalle P Tomar H Bhardwaj D Acharya andA Bhardwaj ldquoA lstm based deep learning network for rec-ognizing emotions using wireless brainwave driven systemrdquoExpert Systems with Applications vol 173 Article ID 1145162021

[30] A M Bhatti M Majid S M Anwar and B Khan ldquoHumanemotion recognition and analysis in response to audio musicusing brain signalsrdquo Computers in Human Behavior vol 65pp 267ndash275 2016

[31] Y Bengio P Simard and P Frasconi ldquoLearning long-termdependencies with gradient descent is difficultrdquo IEEETransactions on Neural Networks vol 5 no 2 pp 157ndash1661994

[32] H B Mann and D R Whitney ldquoOn a test of whether one oftwo random variables is stochastically larger than the otherrdquoeAnnals of Mathematical Statistics vol 18 no 1 pp 50ndash601947

10 Computational Intelligence and Neuroscience