www.elsevier.com/locate/ijpsycho
International Journal of Psychop
Beta oscillations in face recognition
Murat Ozgorena,b, Canan BaYar-Eroglub,c, Erol BaYara,b,*
aBrain Dynamics Multidisciplinary Research Center and Faculty of Medicine, Department of Biophysics, Dokuz Eylul University,
Izmir 35340, TurkeybTUBITAK Brain Dynamics Multidisciplinary Research Network, Ankara, Turkey
cBremen, Institute of Psychology and Cognition Research and Center for Cognitive Sciences, University of Bremen,
P.O. Box 330440, Bremen D-28334, Germany
Received 4 May 2004; received in revised form 3 June 2004; accepted 8 June 2004
Available online 5 August 2004
Abstract
This report presents an analysis of the brain’s beta oscillations in face recognition. We performed experiments on 26 subjects
with a strategy consisting of two types of stimulations: (1) the picture of an elder anonymous lady (unknown face) and (2) the
picture of the subject’s own grandmother (known face). The subjects were healthy, young people between the ages of 15–32
years. Data were analyzed by means of amplitude frequency characteristics and digital filtering. Our results show the significant
role of beta response in face recognition and the differentiation of known and unknown faces. Furthermore, this report supports
our former view that the presentation of grandmother face evokes selectively distributed multiple oscillations in the brain.
Together with the scope of other frequencies (e.g., delta, theta, and alpha), this method can serve as a tool for research studies or
clinical studies in memory and cognition.
D 2004 Elsevier B.V. All rights reserved.
Keywords: Beta; Delta; Alpha; Theta; Oscillations; ERP; Face recognition; Semantic; Episodic; Memory; Laterality; Prolongation
1. Introduction
The aim of the present study is to investigate the
role of beta oscillations in face recognition. In our
long-standing work, we have empirically shown that
the integrative brain functions are manifested with the
0167-8760/$ - see front matter D 2004 Elsevier B.V. All rights reserved.
doi:10.1016/j.ijpsycho.2004.06.005
* Corresponding author. Brain Dynamics Multidisciplinary
Research Center and Faculty of Medicine, Department of Bio-
physics, Dokuz Eylul University, Izmir 35340, Turkey. Tel.: +90
232 4124480; fax: +90 232 27291888.
E-mail address: [email protected] (E. BaYar).
superposition of multiple oscillations. In order to give
further experimental support to this working hypoth-
esis, we recently have started measurements with
facial stimuli and described that delta, theta, and alpha
responses of the brain have clear differentiation upon
presentation of known and unknown faces (BaYar et
al., 2001, 2004; BaYar, 2004).
According to some authors, face selective activity
takes place in fusiform gyrus, which has also been
known as face fusiform area (Klopp et al., 1999;
Kanwisher et al., 1998). However, a number of studies
suggest that not only the occipito-temporal areas but
hysiology 55 (2005) 51–59
M. Ozgoren et al. / International Journal of Psychophysiology 55 (2005) 51–5952
also other areas in the brain are activated during face
recognition (Begleiter et al., 1995; Schweinberger et
al., 2002; Eimer, 2000).
The brain’s beta oscillatory responses remained in
the shadow of the analyses in the gamma band. These
oscillations, however, merit important consideration.
As the present study clearly shows, the distributed
beta oscillatory responses play an essential role in face
recognition and the differentiation of known and
unknown faces.
2. Materials and methods
2.1. Experimental strategy
We used a strategy consisting of application of
three different types of stimulation:
1) A simple light stimulation as control signal: its
luminance was approximately at the same level of
stimulations 2 and 3 described in the following
(approximately 30 cd/m2).
2) The picture of an unknown face (an anonymous
elder lady)
3) The picture of the dknown faceT: the subject’s
own grandmother.
A total of 26 subjects in the age range of 15–36
years (17 females and 9 males) participated in the
study. They had normal or corrected-to-normal
binocular visual acuity and were right-handed. The
pictures of the faces of the subjects’ own grandmother
were prepared prior to measurements. The pictures
were black and white (17�17 cm) and were displayed
on a screen located at a distance of 120 cm from the
subjects. The stimulus duration was set to 1000 ms
with intervals varying between 3.5 and 7.5 s. The
subjects, who were instructed to minimize blinking
and eye movements, sat in a sound-proof and dimly
illuminated echo-free room.
2.1.1. Data recording set
All three stimulations described above have been
applied in a random sequence. Seventy-five stimula-
tion signals have been applied with approximately the
same distribution of each type of stimulation. Follow-
ing the recordings, grandmother’s (known) face,
unknown face, and light responses have been divided
into subsets. In all experiments, subjects reported that
they clearly recognized and differentiated the face of
their own grandmother.
2.1.2. Electrophysiological recording
The electroencephalogram (EEG) was recorded
from F3, F4, Cz, C3, C4, P3, P4, O1, and O2
locations according to the 10–20 system (Jasper,
1958). For the recordings, an EEG-CAP was used.
For the ears, electromyogram (EMG) and electro-
olfactogram (EOG) recordings with Ag/AgCl electro-
des were used. Linked earlobe electrodes (A1+A2)
served as reference. The EOG medial upper and
lateral orbital rim of the right eye was also
registered. The EEG was amplified by means of a
Nihon Kohden EEG-4421 G apparatus with band
limits 0.1–100 Hz, 24 dB/octave. The EEG was
digitized on-line with a sampling rate of 512 Hz and
a total recording time of 2000 ms, 1000 ms of which
served as the prestimulus baseline and was stored on
the hard disc of the computer.
2.2. Data Analysis
Before the averaging procedure, the epochs con-
taining artifacts were rejected by an off-line technique.
The frequency responses of the system were deter-
mined using the amplitude frequency characteristics
(AFC) method (for a more detailed explanation of
AFC method, please see BaYar et al., 2001; KarakaY et
al., 2000).
According to the general systems theory, all
information about the frequency characteristics of a
linear system is contained in the transient response of
the system and vice versa. In other words, knowledge
of the transient response of the system allows one to
predict how the system will react to different
stimulation frequencies if the stimulating (input)
signal is sinusoidally modulated. If the step response
c(t) of the system—in our case, the ERP—is known,
the frequency characteristics G(jx) of this system can
be obtained with a Laplace (or one-sided Fourier)
transform of the following form:
G jxð Þ ¼Zl
0
exp � jxtð Þd c tð Þgf
Fig. 1. AFC as grand averages (N=26). Solid line: Grandmothe
(known) face responses at F4. Dashed line: Unknown face responses
at F4. Along the x-axis: frequency is in logarithmic scale. Along the
y-axis: relative amplitude in decibels. The amplitudes are normalized
in such a way that the amplitude at 1 Hz is equal to 0. Vertical dotted
lines indicate the beta range (15–30 Hz).
M. Ozgoren et al. / International Journal of Psychophysiology 55 (2005) 51–59 53
where G (jx)=frequency characteristics of the system;
c(t)=step response of the system; x=2Sf=angularfrequency, and f=frequency of the input signal.
The frequency characteristics, including the infor-
mation on amplitude changes of forced oscillations
and the phase angle between the output and the input,
are also called the frequency response function. It is a
special case of transfer function and is, in practice,
identical with the transfer function.
For numerical evaluation, a fast Fourier transform
(FFT) is used. Let Xn be a discrete time series
Xn=X(nDt), T=((N�1)Dt). Then the Fourier transform
Yk of Xn is:
Yk ¼ Y ðxkÞ ¼XN�1
n¼0
Xnexpð � i2pN�1nkÞ;
xk ¼ 2pkT�1
where Yk=ak+ibk are the complex Fourier coeffi-
cients, the geometric mean of which is the amplitude
spectrum.
The AFC is expressed in relative units and reflects
the amplification in the studied frequency channels.
The presence of a peak in the AFC thus reveals the
frequency selectivities of the system, and these are
interpreted as its most preferred oscillations when
responding to stimuli. The AFC method has also the
advantage of showing the status of all frequencies in a
combined manner.
2.2.1. Algorithm for evaluation of prolongation of
oscillations
We performed an analysis to evaluate the magni-
tudes and prolongations of the beta responses: the
maximum peak-to-peak (pp) amplitude and the
latency (lat) of the beta responses were measured. In
order to assess the duration and the damping of these
responses, the time periods for reaching half (1/2 lat)
and third (1/3 lat) of the maximum pp amplitudes of
the responses have been measured. This measure
revealed the temporal characteristics of the oscilla-
tions, as the onset, duration, and damping of these
responses. The same procedures were repeated for all
sets and locations.
The Wilcoxon matched pair test was used as a
statistical procedure in order to constitute the dis-
tinction between responses to grandmother and
unknown faces.
3. Results
3.1. Beta responsiveness in the frequency and time
domains
Fig. 1 shows the grand average AFC obtained from
responses to grandmother and unknown faces at F4. It
is noteworthy that the relative amplitude of beta
response to the unknown faces is higher in compar-
ison to grandmother faces. This AFC is also useful to
choose limits of filters for determination of beta
responses in the time domain. As illustrated in Fig. 1,
the frequency response to facial stimuli had more
prominent and distinct maxima in the beta range,
peaking around 22 Hz, in comparison to the gamma
range. Accordingly, we recommend the use of
frequency characteristics in order to determine opti-
mum filter limits (15–30 Hz) instead of using given
templates.
3.2. Beta responses from single subjects and grand
average responses
Beta (15–30 Hz) responses of individual subjects
to grandmother and unknown faces were recorded at
all electrode sites. These responses were similar to
the results obtained from grand averages of 26
r
M. Ozgoren et al. / International Journal of Psychophysiology 55 (2005) 51–5954
subjects. In regard to this similarity, Fig. 2 illustrates
beta responses to unknown face (upper curve) and
grandmother face (lower curve) of a single subject
(subject 16) and (B) grand average responses to
grandmother and unknown faces are illustrated. In
this illustration, the global linear envelopes of
oscillatory responses are also indicated. The prolon-
gation time PT is the time difference between the
decay of responses to grandmother face and the
anonymous face. Latencies evaluated firstly with the
maximal pp response and also with the minimum of
the dampening amplitude, as vertical dashed lines
indicate. Note the smaller amplitude and prolonged
character of the lower curves (beta responses to
Fig. 2. (A) F4 beta responses (15–30 Hz) to grandmother (known)
and unknown faces from a single subject. The asterisks point to the
pp response. (B) F4 grand average beta responses (15–30 Hz) to
grandmother (known) and unknown faces from 26 subjects.
PT=prolongation time. The response to grandmother (known) faces
is represented by a solid line, and response to unknown faces is
represented by a dashed line.
grandmother face). The responses to the unknown
face have larger amplitudes and are less prolonged.
Furthermore, the responses to grandmother faces
showed prolonged oscillations in comparison to the
responses to unknown faces.
3.2.1. Topography of beta responses
In Fig. 3, the topographical distribution of grand
average beta responses to grandmother and unknown
faces is shown. Please note that the responses to
grandmother faces are more prolonged than the
responses to unknown faces at the frontal and central
areas. The latencies of beta responses to grandmother
faces varied between 150 and 220 ms in different
recording sites. Similarly, the latencies of beta
responses to unknown faces varied between 130 and
190 ms in different recording sites.
3.2.2. Amplitudes of beta responses
Statistical analysis showed that amplitudes of beta
responses to unknown face stimuli were significantly
larger than the amplitudes of beta responses to
grandmother stimuli at F3, F4, Cz, and P3 recording
sites (Pb0.05). Accordingly, at the F3 recording site,
the amplitude of beta response to grandmother face
was 0.98 AV pp and the amplitude of beta response to
unknown face was 1.32 AV pp, which was signifi-
cantly larger (P=0.023).
At the F4 recording site, the amplitude of beta
response to unknown face (1.40 AV pp) was
significantly larger (P=0.035) than the amplitude of
beta response to grandmother face (0.98 AV pp). At
the Cz recording site, the amplitude of beta response
to unknown face (0.93 AV pp) was significantly larger
(P=0.020) than the amplitude of beta response to
grandmother face (0.63 AV pp). Also, at the P3
recording site, the amplitude of beta response to
unknown face (0.74 AV pp) was significantly larger
(P=0.023) than the amplitude of beta response to
grandmother face (0.36 AV pp).
According to the statistical analysis, the amplitudes
of beta responses to grandmother and unknown face
stimuli were not significantly different at the record-
ing sites of P4, O1, and O2.
3.2.3. Laterality at parietal electrodes
The beta response amplitudes were also compared
in between two hemispheres for each stimulation and
Fig. 3. Topography of the grand averages of filtered (15–30 Hz) EEG–ERPs to the known face (own grandmother) and unknown face
(anonymous) stimuli. Beta response to grandmother faces is represented by a solid line, and beta response to unknown faces is represented by a
dashed line. Stimulation applied at d0 msT time point.
M. Ozgoren et al. / International Journal of Psychophysiology 55 (2005) 51–59 55
location. This analysis was significant only for the
beta response to grandmother stimuli at parietal
locations: there was significant difference between
the amplitudes of beta responses to grandmother face
at P3 and P4 recording sites (P=0.017).
3.2.4. Prolongation of beta responses
An important feature of the results is the significant
differentiation of beta responses along time axis. In
Fig. 4, the prolongations of beta responses in different
recording sites are indicated. As this figure shows, the
Fig. 4. The time periods for reaching a third of the maximum
amplitude values of grand average filtered beta (15–30 Hz)
responses to different face stimuli. Beta responses to grandmother
(known) faces are represented by black bars, and beta responses to
unknown faces are represented by gray bars.
M. Ozgoren et al. / International Journal of Psychophysiology 55 (2005) 51–5956
responses to grandmother faces are more prolonged in
comparison to responses to unknown faces at F3, F4,
and Cz.
Accordingly, at the F3 recording site, the time
period for reaching a third of its pp amplitude (1/3
lat) of beta response to unknown face was 255 ms
and to grandmother face was 400 ms. Thus, the beta
response to grandmother face was significantly more
prolonged than the beta response to unknown face
(P=0.004). Similarly, at F4, the beta response to
unknown face was prolonged to 275 ms and the beta
response to grandmother face was prolonged to 400
ms. At this recording site, there was significant
prolongation in favor of beta response to grand-
mother face (P=0.006). The largest prolongation
(575 ms) was of the beta response to grandmother
face at Cz, which was also significantly more
prolonged than the beta response (260 ms) to the
unknown face (P=0.028).
The other recording sites did not show significant
changes of prolongations of beta responses to grand-
mother and unknown faces.
4. Discussion
4.1. Selectively distributed beta responses
Several research groups have reported differ-
entiated ERP components during face presentation
localized at temporal areas (Botzel et al., 1989;
Bentin and Golland, 2002; Eimer, 2000; Zhang et al.,
2001). Only a few papers are related to the frontal
regions during face recognition. Begleiter et al.
(1995) showed that for recognition of familiar faces,
both the temporal and frontal regions are involved.
Schweinberger et al. (2002) also reported that only
for the familiar faces, responses were recorded from
parietal, central, and prefrontal areas.
Our results showed that the beta responses to both
grandmother faces and unknown faces were widely
distributed at all recording sites. At F3, F4, Cz, and P4
areas, the maximal amplitudes of beta responses to
grandmother faces were smaller than the maximal
amplitudes of responses to unknown faces. However,
at P4 and O2, the beta responses to grandmother faces
were larger than unknown face responses. The
selective distribution of beta responses in all recording
sites is suggestive that not only temporo-parietal
regions but also the cortex are involved during the
processing of faces.
4.2. Beta responses in time domain
A major part of the papers indicated the
presence of face-specific responses in the range
of 120–210 ms (Botzel et al., 1989; Linkenkaer-
Hansen et al., 1998; Bentin and Golland, 2002). In
a recent study, potentials specific to familiar faces
extending to 400–600 ms were recorded as the
largest at midline electrodes (Eimer, 2000). It was
suggested that those results might reflect the
activation of stored representations of familiar
faces.
Our data show that the beta responses to known
faces vary between 150 and 220 ms and beta
responses to unknown faces are between 130 and
190 ms. These results are in agreement with earlier
findings reporting face specific activity between
120 and 210 ms (Botzel et al., 1989; Linkenkaer-
Hansen et al., 1998). Beta responses to grand-
mother faces had prolonged oscillations in compar-
ison to beta responses to unknown faces at F3, F4,
and Cz. Furthermore, beta response prolongations
(extending to 575 ms) do occur with the same
latency of the late evoked potential components
(400–600 ms) that are specific to familiar faces
(Eimer, 2000).
M. Ozgoren et al. / International Journal of Psychophysiology 55 (2005) 51–59 57
4.3. Laterality of responses
In a study of interhemispheric cooperation, Mohr et
al. (2002) obtained a highly significant familiarity by
visual field interaction showing that, only for familiar
faces, a bilateral advantage was obtained. Schutter et
al. (2001) reported parietal asymmetry in beta
frequency.
Our results indicate that the amplitudes of
responses to grandmother faces (but not to unknown
faces) show significant differences only between P3
and P4 recordings. Our results are in accordance with
studies of Schutter et al. (2001) who showed
asymmetries in the beta range.
4.4. Multiple functions of beta oscillations
Various functions have been assigned to beta such
as visual attention (Marrufo et al., 2001; Wrobel,
2000), movement-related changes (Muller et al.,
2003), excitation–inhibition (Whittington et al.,
2000), sensory memory (Haenschel et al., 2000),
cognitive tasks (Ray and Cole, 1985), and recollection
and familiarity (Burgess and Ali, 2002). Recently,
linkage and linkage disequilibrium between beta
frequency and a GABAA receptor gene have been
reported (Porjesz et al., 2002). Rangaswamy et al.
(2002) reported enhanced beta power in the resting
EEG of alcoholics.
4.5. Multiple oscillations in face recognition
Polich and Kok (1995) emphasized the factors
that contribute to neuroelectric activity related to
cognitive processes besides attention allocation and
immediate memory. In a study of cognitive process-
ing, Klopp et al. (1999) analyzed face-selective
spectral changes of ERPs. They concluded that
phase locking of event-related spectral power took
place not within a narrow frequency band but across
a broadband of frequencies from 5 to 45 Hz, thus
including the beta range and also the other major
frequency bands.
In most of the face-related reports, only the
amplitudes and latencies of conventional evoked
potentials were mentioned. The sensory and cogni-
tive processes related to facial stimuli of the brain
would need to be more complicated. The whole
brain and all oscillations are activated during
recognition or remembering of one’s own grand-
mother and an anonymous face. The ensemble of
responses behaves like a three-dimensional con-
struct, consisting of temporal, spatial, and frequency
spaces (BaYar, 2004). The responses to an anony-
mous face or to a grandmother face are not
represented solely by one location and a unique
frequency or at the same position along the
temporal axis, as differentiated delay and prolonga-
tion of multiple oscillations that are selectively
distributed on the whole cortex show (BaYar et al.,
2004).
Although beta responses have larger amplitudes
upon presentation of unknown faces, there is more
prolonged activity in the beta range upon presentation
of grandmother faces. On the contrary, theta responses
are larger and less prolonged to grandmother faces in
comparison to theta responses to the unknown faces
(BaYar, 2004).
Klimesch et al. (1994) and Sauseng et al. (2002)
showed the possibility of differentiating the role of
alpha and theta oscillatory activities during memory
tasks. Alpha activity has been also correlated with
phyletic memory, as well as episodic and semantic
memories, and selectively distributed over several
cortical areas (BaYar, 1999, 2004).
In the delta frequency range, significant amplitude
increases were observed in posterior areas to
responses upon grandmother and unknown face
stimuli, but not in frontal areas (BaYar et al., 2004).
The differentiation of the two types of facial stimuli is
more distinct in beta responses than alpha and delta
responses especially at the frontal and central areas,
thus showing the important role of beta oscillations in
face recognition.
5. Conclusion
(1) During activation of episodic memory (grand-
mother picture) and semantic memory (anony-
mous face), the topologic distribution of beta
responses is significantly differentiated by show-
ing varied degrees of amplitude enhancements
and prolongations.
(2) These results support and extend the concept that
integrative brain functions are based on multiple
M. Ozgoren et al. / International Journal of Psychophysiology 55 (2005) 51–5958
oscillations selectively distributed in the whole
brain.
(3) During face recognition, which is also involved
in memory differentiation, a parallel processing
in the beta frequency range takes place. Later-
ality, prolongation of oscillations, and topolog-
ical selectivity belong to the manifestations of
this parallel processing.
(4) Besides the parallel processing in beta frequency
range, the superposition of oscillations in delta,
theta, and alpha frequency windows indicates
that the recognition of faces is manifested with a
multifold of oscillatory activities including
prolongation of oscillations.
(5) This high degree of differentiation in recognition
of faces, which is reached by the present
analysis, cannot be realized by conventional
ERP analysis.
Acknowledgements
The experiments of this collaboration study were
supported by TUBITAK-DFG grant 446 TUR 112/14/
01, DEU 02 KB.SAG.012/ 027-03.KB.SAG.064.
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