Introduction

It is widely accepted that EEG signals vary as a func-tion of the brain state and of different topographicareas. EEG signals reflect spatiotemporal patterns ofthe electrical activity of the brain that consist of aseries of short-lasting quasi-stationary epochs. Thebrain processes information in a parallel fashionwhere several neuronal assemblies are simultaneouslyactive. These neuronal networks are not contiguousand may occupy cortical areas of different extent sizes and positions, producing EEG activities withcomplex spatio-temporal patterns.

It is well known that learning disabled childrenhave a slower EEG than normal children. This studyexplored whether incorrect responses were associatedwith more slow activity than correct responses innormal children.

Several authors have analysed the relationshipbetween EEG frequency and the performance ofdifferent tasks. In general, a lower EEG frequencyhas been related to longer reaction time (RT) or toa greater number of errors.1–3 The most commonhypothesis was that EEG–performance relationship

was modulated by the alertness level.1,4,5 It is beyondany doubt that alertness level produces changes inboth EEG and performance. However, previousreport in normal children has shown a positive corre-lation between EEG delta power at rest and RT anderror rate in visual attention and memory tasksperformed in different sessions.6 In this study therelationship between EEG and performance whenalertness has been maintained throughout the task hasbeen explored.

EEG rhythms have traditionally been classified indifferent bands according to their frequency. Deltaactivity (1.5–3. 5 Hz) is the main characteristic of slow wave sleep, but it may also be present duringattention to internal processing such as mental calcu-lation and memorization.7,8 Based on previousobservations, we predicted that incorrect responseswould be preceded by more delta activity than correctones. Theta activity (4– 7 Hz) may reflect a gatingfunction on the flow of information through thehippocampus and the circuits of target structures.EEG power below 6– 7 Hz in the pre-stimulusinterval has been positively correlated with errorrate.2 As with delta, we may assume that more theta

Cognitive Neuroscience

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THIS study shows that incorrect responses are precededby different EEG characteristics than correct responses,and that these differences appear in specific brain regionsthat participate in each particular task. EEGs wererecorded in children during three different tasks: colordiscrimination (CDT), verbal working memory (VWM),and word categorization task (WCT). EEG segmentsprevious to the presentation of the stimulus wereanalysed. Incorrect responses were preceded by lowerEEG power values at 7.8 Hz in posterior temporal andright parietal leads in CDT, 8.59 and 9.36 Hz in frontalareas in VWM, and 10.72 Hz in the left hemisphere inWCT. In the former task > 1.56 Hz power in frontalareas prior to an incorrect response was also observed.NeuroReport 9: 3681–3687 © 1998 Lippincott Williams &Wilkins.

Key words: Alpha; Attention; Delta; EEG; Memory;qEEG; Performance; Semantic categorization; Sourceanalysis

Relationship of specificEEG frequencies atspecific brain areas withperformance

Thalía Fernández,CA Thalía Harmony,1

Juan Silva,1 Lídice Galán,2

Lourdes Díaz-Comas,2 Jorge Bosch,2

Mario Rodríguez, Antonio Fernández-Bouzas,Guillermina Yáñez, Gloria Otero3 andErzsébet Marosi

ENEP Iztacala and 1Centro de Neurobiología,Universidad Nacional Autónoma de México,José Clemente Orozco 36-2, Ciudad de losDeportes, México D.F. 03710; 2Centro deNeurociencias de Cuba; 3UniversidadAutónoma del Estado de México

CACorresponding Author

Website publication 19 November 1998 NeuroRepor t 9, 3681–3687 (1998)

prior to the presentation of the stimuli will be acharacteristic of the incorrect responses. In relationto alpha (8–13 Hz) activity, the presently acceptedview is that it is more than just a spontaneous rhythmand that it is a prototype of a dynamic process whichgoverns a large ensemble of integrative brain func-tions.9 Alpha patterns can be spontaneous, inducedor evoked by a stimulus, movement-related ormemory-related and emitted. Basar et al.9 reportedthat well trained subjects emitted bursts of alpha bandenergy for up to a full second before the delivery ofthe expected target, in a task where subjects had topredict the occurrence of omitted signals. On theother hand, the different frequencies within the alpharange seemed to serve different functions: Klimesch10

proposed that upper alpha band activity is particu-larly sensitive to semantic memory demands and thatthe lower alpha band seems to reflect attentionalprocesses. Beta activity (15–30 Hz) has also beenrelated to cognitive activity.11 Townsend andJohnson4 described a reduction in activity in the 15-20 Hz range preceding errors of omission. We didnot study gamma activity, due to the characteristicsof our amplifiers, and it will not be discussed in thispaper.

From the above mentioned references it is clearthat one would expect differences in the frequencycontent of EEG segments preceding correct andincorrect performance during sustained attention andin the absence of drowsiness. All these results clearlysuggest that a narrow band analysis is more usefulthan a broad band EEG analysis and the recordingof as many leads as possible for the evaluation ofEEG–mental activity interrelationships. However,there are few reports on this topic analysing a widerange of frequencies, and for this reason we usednarrow band spectral analysis in order to detectdifferences in specific frequencies between the EEGpreceding correct and incorrect responses. Anothergoal of this study was to demonstrate that fordifferent tasks involving different networks thedifferences in the EEG previous to correct and incor-rect performance are in different brain regions. Ourhypothesis is that the EEG activity recorded on leadsshowing differences between correct and incorrectresponses in specific tasks will have a topographyspecific to the task, as demonstrated by convergingevidence from brain lesions, EEG, PET and func-tional MRI.

Materials and Methods

Subjects: Forty-seven normal 8- to 10-year-old chil-dren were studied (24 females). These children hadnormal neurological examination with normal vision,

normal EEG at rest with eyes closed, normal CTscans and normal IQ (WISC-RM, 103.88 ± 12.04). All children had the same socioeconomic status andno pathological antecedents. They were selected assubjects because in this age range correlations havebeen clearly observed between EEG spectral para-meters at rest and performance in attention andmemory tasks.8

Tasks: The time chart for each task was designed sothat it would be possible to select EEG segmentshundreds of milliseconds after the response to the pre-vious stimulus and immediately prior to the upcomingstimulus (Fig. 1). In all tasks subjects used both hands(counterbalanced) in go–go paradigms. The colordiscrimination task (CDT) was presented as a videogame, included in the mind tracer system (TM). Thechild was flying a spacecraft and he/she had to oper-ate a button to shoot violet spaceships (targets) andanother button to shoot spaceships in another fivecolors (non-targets). If the response was correct thespaceship exploded; otherwise his/her own spaceshiplost gas. The frequency rate of target vs non-targetpresentation was 25/75. The duration of the stimuliwas 200 ms with an interstimuli interval of 2000 ms.The maximum accepted reaction time was 900 ms.Responses with larger RT were evaluated as omis-sions. Four hundred trials were used. For the verbalworking memory task (VWM) we used a modification

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FIG. 1. Time chart for each task. CDT: EEG segment was selected1280 ms previous to the presentation of the stimulus. VWM: the EEGsegment selected was previous to the presentation of the memoryset. WCT: the EEG segment was selected previous to the presenta-tion of the word. The maximum RT allowed in CDT and the meanRT (MRT) for each task are shown. The EEG segments selected werenot contaminated by the motor response.

of Sternberg’s paradigm.13 Each trial in this experi-ment began with a visual warning stimulus (*) with aduration of 300 ms. After a postinterval time of 2 s, amemory set of either three or five digits was presentedon a video monitor for 1500 ms and 2 s after a singledigit was displayed for 300 ms. The subject had torespond with one button if the digit was in the mem-ory set and with another button if it was not. Theinterval between trials was of 3 s. Two hundred trialswere presented: in 50% the digit belonged to thememory set. The child was told whether the responsewas correct or incorrect. The EEG segment wasselected prior to the presentation of the memory set(Fig. 1). In the word categorization task (WCT) aword was presented on the video monitor for 2000ms. The subject has to press one button if it was ananimal word and another if it was not. The intervalbetween words was 5 s. Two hundred trials were pre-sented: 25% corresponded to an animal and 75% werenon-animals. The words presented in this task wereobtained from a pilot study where the subjects namedstandardized figures of Snodgrass and Vanderwart.13

The child was told whether the response was corrector incorrect.

Behavioral responses: Behavioral responses wereanalysed in order to be sure that the vigilance levelwas sustained throughout the task: the number ofincorrect responses and the RT should not increaseat the end of the experiment and the RT to theincorrect responses should not be longer than tocorrect responses. We assumed that if these condi-tions were accomplished, there was no deteriorationin vigilance.

Recordings: The EEG was recorded with referenceto linked ears from Fp1, Fp2, F3, F4, C3, C4, P3,P4, O1, O2, F7, F8, T3, T4, T5, T6, Fz, Cz, Pz andOz. The EOG was recorded from a supraorbital elec-trode and from an electrode placed in the externalcanthus of the right eye. The amplifier bandwidthwas set between 0.05 and 30 Hz. The EEG wassampled every 5 ms using a Medicid 3E System andstored on a hard disk for further analysis. Subjectswere seated in a comfortable chair in front of thevideo monitor. Stimuli were delivered by a mindtracer system synchronized to the Medicid 3E acqui-sition system. EEG segments of 1280 ms wereselected immediately before the stimuli since it hasbeen demonstrated that this interval is the best forpredicting performance.1

Frequency analysis: For each monopolar lead, the fast Fourier transform was computed and the power, at each 0.78 Hz, was calculated (from 0.78 to19.5 Hz).

Statistical analysis: Not all power values have anormal distribution after logarithmic transformation.Thus, parametric analyses as rmANOVAs were notrecommended. The statistical significance of thepower data between conditions (correct vs incorrect)was assessed by a multivariate non-parametric per-mutational test.14,15 The global null hypothesis testedwas the equality between power spectra recorded intwo different conditions (correct vs incorrect) in allderivations and all frequencies. The marginal nullhypotheses were the equality of the power at aparticular frequency in different locations, as well asfor different frequencies for a given location. For the analysis of behavioral results, t-tests were used.

Source analysis: Frequency domain variable resolu-tion electromagnetic tomography (FD-VARETA)16

was used to calculate the distributed sources for eachfrequency. This is a recently developed technique forestimating the source generators of EEG data. It is adiscrete spline distributed solution that imposesdifferent amounts of spatial smoothness for differenttypes of generators and restricts current sources to gray matter by use of a probabilistic mask thatprohibits solutions where the mask is zero. The probabilistic brain atlas used was developed at the Montreal Neurological Institute.17

Results

As not all subjects showed more than eight EEGsegments without artifacts previous to incorrectresponses in all tasks, the number of subjects was different for each task and thus each task wasanalysed independently.

Color discrimination task: Table 1 shows the meanvalues and the significant differences between powerin each condition (n = 44). Incorrect responses werepreceded by EEG segments with higher power at 1.56 Hz in Fp1, Fp2, F7 and F8, at 3.12 Hz in C4,and at 5.46 Hz in F8. However, incorrect responseswere also preceded by EEG with less power at 7.8Hz in P4, T5 and T6 and at 14.82 Hz in T6. Tocorroborate the localization of the EEG activity ineach frequency, the distributed sources were calcu-lated by FD-VARETA, and the spatial distributionof the sources of the EEG previous to correct andincorrect responses was compared by paired t-tests.Figure 2 shows these differences for 1.56 Hz. Oneobserves that incorrect responses were preceded byan EEG with more activity at 1.56 Hz in frontalregions. In general, the locations of the differences at the scalp corresponded to similar locations in the underlying cortex of the differences in the spacelocation of the sources.

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Behavioral results are presented in Tables 2 and 3.The RT to the correct responses was larger than tothe incorrect ones. No significant differences werefound in the number of errors between the first andthe last 50 trials, suggesting that no fatigue wasobserved. The RT to the last 50 trials was shorterthan the RT to the first 50, suggesting the effect ofpractice.

Verbal working memory task: Table 1 shows theresults obtained in 42 subjects. Incorrect responseswere preceded by EEG segments with less power at 8.58 Hz in F7, at 9.36 Hz in Fp1, Fp2 and F4, at18.72 Hz in Fz and at 19.50 Hz in F3. Figure 3 showsthe distribution in the brain of the differences in spacelocation of the sources at 9.36 Hz between the EEGprevious to the correct and incorrect responses. TheRT was smaller for the correct responses than for theincorrect ones (Tables 2 and 3). However, when thelevel of difficulty was analysed, we found that 87%of the incorrect responses occurred when there werefive digits to remember. However, in the correct onesthere was the same proportion of memory sets withthree and five digits. Thus, in this case, the longerRT to the incorrect responses was associated with agreater memory set, a very well known finding.13 Theother behavioral results showed no significant differ-ences in the number of errors or in the RT betweenthe first and last 50 trials (Table 3).

Word categorization task: Twenty-four subjectswere analysed. EEG segments previous to theincorrect responses showed less power at 7.02 Hz inT3, at 7.80 Hz in F7, at 8.58 Hz in F3, at 9.36 Hz inO1 and T5 and at 10.92 Hz in T3 (see Table 1). Figure4 illustrates the differences between the space loca-tions of the sources at 10.92 Hz. The behavioralresults showed no differences in the RT to correctand incorrect responses, no differences in the numberof errors between the first and last 50 trials, and lower RT in the first 50 than in the last 50 trials(Tables 2 and 3).

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Table 1. Significant differences between EEG power previous to correct andincorrect responses

Task Frequency Lead log AP log AP p(Hz) correct incorrect

CDT 1.56 Fp1 7.50 ± 0.36 7.69 ± 0.50 0.0009Fp2 7.46 ± 0.36 7.70 ± 0.47 0.0009F7 7.07 ± 0.39 7.22 ± 0.51 0.0496F8 7.07 ± 0.41 7.25 ± 0.52 0.0009

3.12 C4 7.24 ± 0.37 7.32 ± 0.51 0.03495.46 F8 5.56 ± 0.47 5.74 ± 0.46 0.0329

P4 6.80 ± 0.90 6.72 ± 0.88 0.04937.80 T5 6.24 ± 0.88 6.08 ± 0.88 0.0480

T6 6.24 ± 0.79 6.07 ± 0.75 0.000914.84 T6 4.53 ± 0.49 4.43 ± 0.40 0.0320

VWM 8.58 F7 5.53 ± 0.72 5.34 ± 0.76 0.0469Fp1 6.00 ± 0.69 5.80 ± 0.71 0.0009

9.36 Fp2 5.93 ± 0.75 5.71 ± 0.73 0.0469F4 6.45 ± 0.72 6.27 ± 0.74 0.0469

18.72 Fz 4.35 ± 0.44 4.18 ± 0.49 0.046919.50 F3 4.54 ± 0.52 4.38 ± 0.53 0.0009

WCT 7.02 T3 7.28 ± 1.44 7.03 ± 1.44 0.00097.80 F7 7.22 ± 1.51 6.99 ± 1.53 0.03298.58 F3 7.80 ± 1.47 7.53 ± 1.45 0.00099.36 O1 9.36 ± 1.56 9.09 ± 1.58 0.0009

T5 8.51 ± 1.44 8.24 ± 1.53 0.000910.92 T3 6.84 ± 1.76 6.50 ± 1.59 0.0369

FIG. 2. Color discrimination task. Statistical parametric mapping: 3-dimensional distribution of the paired t values resulting from thecomparison between the sources at 1.56 Hz of the EEG segmentsprevious to correct and incorrect responses. Blue color indicates thatpower in the EEG segments previous to the incorrect responses wasgreater than to correct responses. The most significant t values wereobserved in prefrontal areas. The t scale is from –3.89 to 3.89.

Discussion

Tasks were selected in order to explore differentnetworks. The CDT explored sustained attention(frontal activation) and color discrimination (ventralstream of the visual system); VWM using Sternberg’sparadigm participation of the frontal lobes is neces-sary; and WCT explores lexical access and lexicalintegration of words. In this task activation of theleft hemisphere is expected mainly in the periSylvianarea.

In the three tasks the EEG that preceded thecorrect responses was faster than the EEG precedingthe incorrect ones. This was manifested in the 3 tasks

by more power between 7.02 and 10.92 Hz in theEEG that preceded the correct responses. It wasparticularly interesting that the regions where signif-icant differences were observed between conditionswere different for each task and correspondedprecisely to our expectations. FD-VARETA16 wasuseful for the location of EEG sources at eachfrequency. In the CDT, the finding of lower powerat 7.8 Hz in posterotemporal leads associated to the EEG preceding the incorrect responses may be related to the participation of these areas in color discrimination.18,19 In addition, less 7.8 Hzpower in the right parietal cortex was associated withfailure in performance: during the task, children

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Table 2. Mean reaction time.*

Task Correct responses Incorrect responses Global RT

CDT 587.45 ± 62.11 492.86 ± 72.54 581.09 ± 63.10VWM 1246.21 ± 218.17 1327.50 ± 288.54 1252.76 ± 227.13WCT 1695.33 ± 449.95 1800.04 ± 626.76 1702.25 ± 463.71

*For each subject, the median of the RT was considered for the averageof the whole group.Values are mean ± s.d.

Table 3. Significant differences of behavioral results between correctand incorrect responses.

Task All trials correct– First–last First–last 50 trials incorrect RT 50 trials RT number of errors

CDT 0.0001 0.0004VWM 0.0040WCT 0.0225

FIG. 3. Verbal working memory task. Statistical parametricmapping: 3-dimensional distribution of the paired t values resultingfrom the comparison between the sources at 9.36 Hz of the EEGsegments previous to correct and incorrect responses. Red colorindicates that power in the EEG segments previous to the correctresponses was greater than to incorrect responses. The most signif-icant t values were observed in prefrontal areas. The t scale is from–2.54 to 2.54.

FIG. 4. Word categorization task. Statistical parametric mapping: 3-dimensional distribution of the paired t values resulting from thecomparison between the sources at 10.92 Hz of the EEG segmentsprevious to correct and incorrect responses. Red color indicates thatpower in the EEG segments previous to the correct responses wasgreater than to incorrect responses. The most significant t valueswere observed in periSylvian cortex. The t scale is from –3.76 to3.76

were required to focus attention on the place wherespaceships appeared. In this process, the right pari-etal cortex has a very well known role. These resultssuggest that activity at 7.8 Hz is necessary in orderto respond correctly in a task demanding attentionto spatial location and color discrimination. Ourfinding that within the alpha band, 7.8 Hz in partic-ular was the frequency on which differences betweencorrect and incorrect responses were observed,supports Klimesch’s proposal11 that the lower alphaband seems to reflect attentional processes. In thistask higher power was also observed at slow frequen-cies (1.56, 3.12, 5.46 Hz) in frontal regions precedingincorrect performance. Rezai et al.20 described thebilateral activation of the frontal mesial cortex inhealthy volunteers during a CDT that used asequence of colored letters as target. We consider thata higher amount of slow activity in frontal regionsmay interfere with the general process of attentionto the task and/or with the process of spatial atten-tion. This process activates the frontal areas and theparietal regions. On the basis of the literature, oursupposition was that the RT to correct responsesshould be shorter than to incorrect ones, and that ifalertness were to decrease progressively, the numberof errors should increase at the end of the task. Thebehavioral results observed in the CDT stronglysuggest that the level of alertness was not decreased.The finding of smaller RT to the incorrect responsesthan to the correct ones may be due to impulsivebehavior by the children. The decrease of RT at theend of the experiment is more suggestive of activelearning than of a decrease in alertness.

On the VWM task, less power at 8.58, 9.36, 18.72and 19.5 Hz in frontal regions prior to incorrectresponses was observed. Petrides et al.21 reported thatareas 46 and 9, corresponding to the middle dorso-lateral frontal gyri of both frontal lobes, are related toverbal working memory. In an experiment in whichthe Sternberg paradigm was used, with six consonantsas memory set, Paulesu et al.22 observed bilateralactivations in the prefrontal cortex. These experimentssupport our findings indicating that a decrease inpower in these areas is a sign of non-optimal condi-tions for producing a correct response. According toKlimesch,10 semantic memory is specifically sensitiveto a decrease in activity in the upper alpha band. Ourresults are in agreement with this hypothesis.

The behavioral results in this task also confirm thatthe factor associated with incorrect responses was nota decrease in alertness. Longer RT to incorrectresponses was found, and we have already explainedthat in this case incorrect responses were observed inthe larger memory set.

In the WCT, the results also agree with our expec-tations: less power at 7.02, 7.80, 8.58, 9.36 and

10.92 Hz was found in the left hemisphere in thefrontal, occipital and temporal areas. According toPET studies, presentation of visual words producesthe activation of extrastriate occipital cortex23 and left temporal areas,24 and the semantic task ofauditory word categorization activates all the areasrelated to language: left temporal, parietal and frontalcortices.25

The behavioral results in this task are harder toexplain: the RT was shorter in the first 50 trials thanin the last 50, suggesting a decrease in the alertnesslevel. However, the number of errors was smaller atthe end of the task (although it did not reach signif-icance, p=.10). Our explanation for this contradictionis that subjects learn to balance errors and RT, tryingto have more correct responses although this implieslonger RT. On the other hand, there were no signif-icant differences in the RT to correct and incorrectresponses, suggesting that a decrease in alertness wasnot a factor involved in poor performance.

In summary, the behavioral results of the threetasks strongly suggest that a decrease in alertness was not a factor responsible for incorrect responses.On the other hand, different spatiotemporal EEGpatterns were observed for correct and incorrectresponses in the three tasks, also supporting thatincorrect responses were not associated with adecrease in alertness level. Functional neuroimagingstudies have demonstrated that during the perfor-mance of different tasks specific structures are acti-vated. Our EEG results indicate that such structuresshould be in a particular state when the incomingstimuli are received. This state is characterized by thegeneration of an EEG with a peculiar frequencypattern. It was also shown that the participation ofthe same structure in different tasks requires differentstates characterized by different frequency patterns(i.e. more 1.56 Hz activity at prefrontal cortex wasobserved previous to incorrect responses in CDT,whereas in VWM the incorrect responses werepreceded by less 9.36 Hz activity than correctresponses in the same regions). These results areimportant for the analysis of the biological factorsthat underlie different cognitive processes, since theyshow that a specific basal state, not related with alert-ness, is necessary in order to respond correctly. Theresults also suggest that subjects with deviant EEGpatterns unrelated to pathological findings, such aslearning disabled children, may not reach this partic-ular state as often as normal subjects, and for thisreason some incoming signals may not be adequatelyprocessed. To our knowledge this is the first timethat a relationship between performance and specificEEG frequencies in those brain regions that belongto the specific network associated with the task isshown.

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Conclusions

According to the task, there are specific EEGfrequencies prior to the presentation of the stimulusthat are related to performance. In order for perfor-mance to be correct, these specific frequencies mustbe present in those brain regions that belong to thespecific network associated with the task.

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press.17. Evans AC, Collins DL, Neelin P et al. Three dimensional correlative imaging,

Applications in human brain mapping. In: Thatcher R, Hallet M, Zeffiro Tet al., eds. Functional Neuroimaging. New York: Academic Press, 1994:145–162.

18. Roland PE. Brain Activation. New York: Wiley-Liss, 1993.19. Corbetta M, Miezin FM, Dobmeyer S et al. J Neurosci 11, 641–649 (1991).20. Rezai K, Andreasen NC, Alliger R et al. Arch Neurol 50, 636–642 (1993).21. Petrides M, Alivisatos B, Evans AC and Meyer E. Proc Natl Acad Sci USA

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ACKNOWLEDGEMENTS: This project was partially supported by GrantsIN214295 and IN208596 from DGAPA and 1028P-H from CONACyT. The authorsacknowledge Vicente Guerrero for the neurological examination of the children,Sandor John for style correction of the manuscript and Miguel Rodríguez-Espinosa, Jorge Bernal and Alfonso Reyes for technical assistance.

Received 5 August 1998;

accepted 4 September 1998

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