Features of Event-related Potentials Used to

Recognize Clusters of Facial Expressions

Masahiro Yasuda

, Zhang Dou

and Minoru Nakayama

Mechanical and Control Engineering, Tokyo Institute of Technology, O-okayama, Meguro-ku, Tokyo, 152–8552 Japan

Human System Science, Tokyo Institute of Technology, O-okayama, Meguro-ku, Tokyo, 152–8552 Japan

Keywords:

Human Emotion, Facial Expression, EEG, ERP, Chronological Analysis, Prediction.

Abstract:

To assess human emotion using electroencephalograms (EEGs), the relationship between emotional impres-

sions of images of facial expressions and features of Event Related Potentials (ERPs) recorded using three

electrodes was analyzed. First, two clusters of emotional impressions were extracted using two-dimensional

responses of the Affect Grid scale. Second, features of ERPs in response to the two clusters were examined.

Time slots where amplitude differences in ERP appeared were measured, and differences in the frequency

power of ERP were also extracted for each electrode. To evaluate these features, prediction performance for

the two clusters was examined using discriminant analysis of the features. Also, the dependency of some band

pass ﬁlters was measured.

1 INTRODUCTION

In order to develop good Human-Computer interfaces

and create good communications systems, such as

human-robot or human-human, the establishment of a

technique for assessing human emotion is necessary.

Various biosignals have been used to try to detect hu-

man emotion (Lin et al., 2010). Electroencephalo-

grams (EEG) are a type of bio-signal sometimes used

to detect human emotion, the feature extraction and

signal processing procedures of which have been pre-

viously discussed (Guitton, 2010; Petrantonakis and

Hadjileontiadis, 2010). In those experiments, cate-

gories of emotion were based on Ekman’s classiﬁca-

tion system (Ekman and Friesen, 1975), and also on

visual stimuli which can evoke the viewer’s impres-

sion of the emotion they are viewing. A simple stim-

ulus for presenting emotion is a set of facial expres-

sions which stimulate the viewer’s impression of the

emotion they are viewing. Even during the recogni-

tion of facial emotions, the transformability of emo-

tions and individual differences should be considered,

however. The relationship between certain facial ex-

pressions and the viewer’s impression of the emotion

they are viewing is sometimes analyzed (Huang et al.,

2009). Therefore, biosignal reactions such as EEGs

should be analyzed in response to the viewer’s im-

pression of the emotion they are viewing. To extract

speciﬁc physiological components of stimuli, Event

Related Potentials (ERPs) are often used to analyze

EEGs. ERP waveforms are averaged waveforms of

EEGs which respond to stimulus. ERPs are often

used for chronological analysis in psychological and

clinical studies (Nittono, 2005; Rugg, 1997). Also,

viewer’s chronological reactions to emotional stimuli

can be analyzed using ERP waveforms. Analysis of

these can contribute to the extraction of good features

used to detect viewer’s emotional responses.

To examine the relationship between the ob-

server’s impressions of emotional face images and

ERP responses when viewing this stimuli, signiﬁcant

feature information from the ERP waveforms was ex-

tracted in an experiment. Therefore, this paper ad-

dresses following topics:

1. The groups of facial emotions which are based on

viewer’s impressions were extracted using subjec-

tive evaluation scores.

2. The ERP waveforms which are based on the emo-

tion groups were compared and the differences

were extracted.

3. To evaluate the signiﬁcance of feature differences

in ERP waveforms, the performance of discrim-

inant analysis was evaluated. Also, the depen-

dency of some band pass ﬁlters was measured.

For these purposes, the following experiment was

conducted.

165

Yasuda M., Dou Z. and Nakayama M..

Features of Event-related Potentials Used to Recognize Clusters of Facial Expressions.

DOI: 10.5220/0005186901650171

In Proceedings of the International Conference on Bio-inspired Systems and Signal Processing (BIOSIGNALS-2015), pages 165-171

ISBN: 978-989-758-069-7

 2015 SCITEPRESS (Science and Technology Publications, Lda.)

2 EXPERIMENTAL METHOD

2.1 Stimulus

The images of facial emotions in this experiment were

prepared using the Japanese and Caucasian Facial

Expression of Emotion (JACFEE) collection (Mat-

sumoto and Ekman, 1988). This collection consists

of 56 color photographs of 56 different individuals

who illustrate one of the seven different emotions:

Anger, Contempt, Disgust, Fear, Happiness, Sad-

ness and Surprise. The photos are of equal num-

bers of Japanese and Caucasian models: 14 Caucasian

males, 14 Caucasian females, 14 Japanese males, 14

Japanese females. The validity of the facial emotions

has been conﬁrmed, but some confusion in recogniz-

ing some of the expressions has also been reported

(Huang et al., 2009).

The subjects (viewers) who participated in this ex-

periment were 6 university students from 19 to 23

years old. Their visual acuity was sufﬁcient to ob-

serve the stimuli. The experimental content was ex-

plained to all participants in advance, and informed

consent was then obtained.

2.2 Procedure

Facial photos as visual stimulus were presented se-

quentially. The experimental sequence is illustrated

in Figure 1. First, a ﬁxation point (+) was shown in

the centre of the screen, and a stimulus appeared after

the screen had been blank for 3-4 seconds. A ﬁxa-

tion point was displayed to attract eye ﬁxation, but it

disappeared before the target stimulus was shown, in

order to trigger stimulus onset in the manner of a con-

ventional visual perception experiment (Kirchner and

Thorpe, 2006). The duration of stimulus display was

3 seconds during which the PC sent a trigger signal to

another recording device. The display sequence was

controlled using Psychtoolbox (Brainard, 1997). A

set of sequences consisted of 56 photos, and the total

duration was 6 minutes. Three trials were conducted

in which different sets are shown to each subject, fol-

lowed by short breaks.

The EEGs were recorded from 3 scalp electrodes

positioned in the Frontal (Fz), Central (Cz) and Oc-

cipital (Oz) areas, according to the international 10-

20 system. The EEG potentials were measured us-

ing a bio-ampliﬁer (ADInstruments: PowerLab4/30,

ML13). The scalp electrodes were referenced to a

base measurement at the subject’s ear lobes. A ground

electrode was placed on the forehead. The following

sampling conditions were used to record signals on a

PC, sampling rate: 400Hz, low pass ﬁlter: 30Hz, time

3 ~ 4s

Figure 1: Diagram of showing stimuli.

1 2 3 4 5 6 7 8 9

Cluster 1 (141)

Cluster 2 (195)

Pleasant

Feelings

Unpleasant

Feelings

High ArousalSleepiness

Figure 2: Results of cluster analysis for viewer’s responses

using the Affect Grid.

constant for a high pass ﬁlter: 0.3sec. To detect blinks

as an artifact source, in addition to the three poten-

tials the vertical component of an electro-occulograph

(EOG) was measured synchronously.

Additionally, ﬁve sets of band pass ﬁlters were

used in this analysis to reduce the artifacts of lower

frequencies in EEGs. The ﬁlter was applied to signals

during off-line processing using LabChart (ADInstru-

ments), which is a zero-phase-lag Finite Impulse Re-

sponse (FIR) ﬁlter. The ranges of band passes were

controlled using the following frequency bandwidths

: 0.5 ∼ 30Hz, 1.0 ∼ 30Hz, 1.5 ∼ 30Hz, 2.0 ∼ 30Hz,

2.5 ∼ 30Hz.

2.3 Subjective Evaluation of Facial

Emotions

In order to evaluate the viewer’s impression of the

emotion they are viewing of the stimulus photos, all

subjects were asked to rate the emotions using a des-

ignated scale. The scale is known as an “Affect Grid”,

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−100 0 100 200 300 400 500 600

−4

−2

Time [ms]

Amplitude [μV]

Figure 3: Three ERPs for Electrodes Fz, Cz and Oz (Band Pass Filter: 2.0 ∼ 30Hz).

and consists of a two dimensional 9 point scales with

“Pleasant - Unpleasant Feelings” and “High Arousal -

Sleepiness” (Russell et al., 1989), as shown in Figure

2. The validity of this scale has often been used to

evaluate facial emotions (Takehara and Suzuki, 2001;

Shibui and Shigemasu, 2005).

All 56 photos were rated by each subject using the

scale, after observing three trial sets of images.

3 RESULTS

3.1 Responses using the Affect Grid

All ratings for the facial image photos using Affect

grid are summarized in Figure 2. The horizontal axis

indicates “Pleasant - Unpleasant Feelings”, and the

vertical axis indicates “High arousal - Sleepiness”.

In regards to Figure 2, the ratings cover most of the

area of the two-dimensional scale in response to the

photos of 7 facial emotions. The responses for two-

dimensional scales were given using integer grading,

and many of the responses overlap others in Figure 2.

In comparing the rating distribution between partici-

pants across two-dimensions, some individual differ-

ences were observed (Yasuda et al., 2014). Though

facial codes are universal emotions used in Ekman’s

study, there are some individual differences in the

ability to recognize them. Therefore, the subject’s re-

sponses were not identical for each photo.

To classify the responses based on their two-

dimensional ratings, cluster analysis using the

weighted pair group method and arithmetic averages

(WPGMA), which is a distance metric used to facil-

itate comparison with each other, was conducted for

all 336 responses (56 photos × 6 subjects). As a re-

sult, two clusters were extracted, and are presented as

two colors in Figure 2. Two clusters are distributed in

the left and right regions of the horizontal axis, which

shows “Pleasant - Unpleasant Feelings”. Therefore,

they can be called “Pleasant” and “Unpleasant” clus-

ters. The ratio of the number of clusters depends on

each subject, and the average ratio for “Pleasant” is

0.42.

3.2 Event Related Potentials

3.2.1 Chronological Analysis of the Two Clusters

To measure the differences in EEG waveforms be-

tween “Pleasant” and “Unpleasant” clusters, the two

were compared chronologically. Here, event related

potentials (ERPs) are calculated to emphasize the

waveforms in response to the two classes of photos

of facial emotions.

EEG waveforms were extracted between -100 and

600 milliseconds before stimulus onset, and the av-

erage voltage in the 100 milliseconds before stimulus

onset was set as a baseline. Trials which contained

artifacts such as blinks or amplitudes over ±100µV

were excluded in advance, and averaged potentials for

the two clusters were then calculated.

First, the ground averages of ERPs are summa-

rized to compare the waveforms of the three elec-

trodes in Figure 3 chronologically. The deviations

start around 100 milliseconds for all ERPs, and this

phenomenon has been observed in previous studies.

The ﬁrst negative peaks were observed around 100

milliseconds in the order of Oz, Cz and then Fz. This

may suggest that visual evoked signals spread from

the Occipital area (low level vision) to the Frontal area

(high level vision). A broad range of waveforms is

observed for Oz while voltage changes for Cz and Fz

appear at around 200 milliseconds. These responses

may reﬂect to the progress of visual information pro-

cessing.

Second, the ERP waveforms between the two

clusters on each electrode are compared in Figure

4. The solid line represents the “Pleasant” cluster,

and the gray line represents the “Unpleasant” clus-

ter. There are some differences in ERPs between the

two clusters. To identify the time zone where there

are signiﬁcant differences between the two means of

ERPs across the two clusters, pair-wise t tests were

FeaturesofEvent-relatedPotentialsUsedtoRecognizeClustersofFacialExpressions

167

−100 0 100 200 300 400 500 600

−4

−2

Amplitude [μV]

−100 0 100 200 300 400 500 600

−4

−2

Amplitude [μV]

−100 0 100 200 300 400 500 600

−4

−2

Time [ms]

Amplitude [μV]

Pleasant

Unpleasant

Figure 4: Comparison of ERPs between two clusters of Electrodes Fz, Cz and Oz (Band Pass Filter: 2.0 ∼ 30Hz), with a

dotted-line box indicating the time zone where there are signiﬁcant differences between two ERPs (p < 0.05).

conducted. In regards to previous studies (Thorpe

et al., 1996; VanRullen and Thorpe, 2001), the time

zone was identiﬁyed where 15 consecutive t test val-

ues were below the p < 0.05 level. In this paper, a 5%

level of signiﬁcance was employed, and d f was 34 (2

clusters × 3 sets × 6 subjects - 2). The sampling slot

mentioned above is 2.5 milliseconds.

The signiﬁcant time slots can be extracted, and

they are illustrated using dotted line boxes in Fig-

ure 4 as follows, Fz: 142.5∼192.5 milliseconds, Cz:

132.5∼195.0 milliseconds. Both are time slots occur-

ing after the negative peaks at around 100 millisec-

onds and before the positive peaks at around 200 mil-

liseconds. Those time slots are independent of the

type of band pass ﬁlter. Therefore, ERPs in those time

slots may contain some features in response to the two

clusters of emotions. At the Oz electrode, the signiﬁ-

cant time slot is too short and too late, so it should be

ignored.

3.2.2 Effectiveness of Band Pass Filters

A band pass ﬁlter is used in EEG measurements to

detect a distinct signal. To determine the appropriate

ﬁlter band to extract features of emotional responses,

the variance of these signals were evaluated using

Variance rate

p<0.05

p<0.01

Figure 5: The rate of variance between two conditions.

analysis of variance (ANOVA). To maximize the vari-

ance between the two clusters, the appropriate band

pass ﬁlter was evaluated using the following proce-

dure. In addition to the randomized factor of the view-

ers, two factors, namely the two clusters and the trial

sets (2 ×3), inﬂuenced the deviations of the potentials

in this experiment. The ratio of variance between the

two clusters (F values) was calculated for the various

ﬁlter conditions.

F values are summarized in Figure 5. The hor-

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6.25 12.5 18.75 25 31.25 37.5

Frequency [Hz]

Amplitude [μV]

Pleasant (T1)

Unpleasant (T1)

Pleasant (T2)

Unpleasant (T2)

Figure 6: Comparison of frequency spectrum for Electrode

Fz across two conditions and time slots (Band Pass Filter:

2.0∼30Hz).

izontal axis represents ﬁlter conditions, the vertical

axis represents F values. Two levels of signiﬁcance in

F values are indicated in Figure 5. The results show

that all conditions for Fz always produce a signiﬁcant

variance ratio, but Electrode Cz needs ﬁlters above

2Hz. Therefore, the band pass ﬁlter should be set over

2Hz to detect the differences in ERPs between the two

clusters.

Additionally, the factor of the trial set is not sig-

niﬁcant and does not inﬂuence other factors in this

analysis.

3.2.3 Frequency Analysis of EEG

In the above section, amplitudes of ERP between the

two clusters are compared. The frequency compo-

nents is another well known feature of EEG/ERP sig-

nals, and the difference in these components should

also be analyzed. Regarding the chronological analy-

sis, typical ERP responses can be observed in the 200

milliseconds after stimuli onset in comparison with

the baseline, which is before onset. To extract some

features of ERP responses, simple frequency analysis

was employed to obtain the frequency factors. Re-

garding measuring restrictions, the features were cre-

ated using the following procedure. Frequency anal-

ysis was applied to two time slots, T1: -100∼60 mil-

liseconds and T2: 60∼220 milliseconds, both were

160 milliseconds span (64 sampling points) whose

length was based on a power of 2 using FFT algo-

rithm. Hanning window was applied to FFT anal-

ysis. Regarding the analytical conditions, the fre-

quency resolution is 6.25Hz.

The amplitudes in the frequency domain for Fz are

summarized in Figure 6. The horizontal axis repre-

sents the frequency components which are based on

the frequency resolution in this analysis. The vertical

6.25 12.5 18.75 25 31.25 37.5

Frequency[Hz]

Amplitude [μV]

Pleasant (T1)

Unpleasant (T1)

Pleasant (T2)

Unpleasant (T2)

Figure 7: Comparison of frequency spectrum for Electrode

Cz across two conditions and time slots (Band Pass Filter:

2.0∼30Hz).

axis represents the amplitude of the components. The

amplitudes in the frequency domain for Cz are sum-

marized in Figure 7, and shown in the same format as

Figure 6. The amplitudes of slot T2 are larger than

the ones for slot T1, and the amplitudes of the ﬁrst

four components in T2 account for over 90% of the

total. The differences in the amplitudes between the

two clusters for both Fz and Cz at 6.25Hz are remark-

able.

3.3 Estimation of Emotion Clusters

using Features of ERP

There are some features of ERPs which present two

emotion clusters, as mentioned in the above sections.

To measure the signiﬁcance of these features, discrim-

inant analysis to predict the emotion clusters using the

features was conducted. A Support Vector Machine

(SVM) was applied to this prediction, and the sub-

ject leave-one-out procedure was employed to eval-

uate the estimation accuracy. The features follow 6

metrics as input data: amplitudes of ERP for each

signiﬁcant time slot for Fz and Cz, and amplitude dif-

ferences in the lower four frequency components be-

tween time slots T1 and T2. The actual numbers of di-

mensions for features are 18 amplitudes of Fz, 25 am-

plitudes of Cz, and 4 frequency components of elec-

trodes Fz and Cz, as mentioned in subsection 3.2.3.

The number of dimensions is summed up when both

Fz and Cz are employed. The feature set consists of

36 pieces of data (2 emotion clusters × 3 set of trial ×

6 subjects). There are 6 prediction conditions : ERP

amplitudes (Fz, Cz, and both) and Frequency compo-

nents (Fz, Cz, and both).

The levels of accuracy are compared between con-

ditions by employing ﬁve band pass ﬁlters mentioned

FeaturesofEvent-relatedPotentialsUsedtoRecognizeClustersofFacialExpressions

169

Accuracy

p<0.05

Figure 8: Accuracy of prediction for emotion clusters using

features of ERPs.

above in Section 2.2. They are summarized in Fig-

ure 8. The horizontal axis represents the ﬁve band

pass ﬁlters, and the vertical axis represents the accu-

racy. This accuracy gradually increases as the band

pass ﬁlter conditions vary toward lower frequences

(0.5∼2.5Hz). To evaluate the signiﬁcance of the pre-

diction, a χ

test was conducted. The level of signiﬁ-

cance in Figure 8 is indicated using a dotted line.

As a result, the level of accuracy of the four condi-

tions is signiﬁcant. Combinations of frequency com-

ponents for Fz and Cz are signiﬁcant for the level of

accuracy while certain ERP amplitudes for Fz and Cz

are effective. From these accuracy results, the low-

est frequency of the band pass ﬁlter should be set at

around 2.0Hz, as this will also maximize the variance

in ERP amplitudes between conditions.

4 DISCUSSION

To evaluate viewer’s emotion, a detectable class of

emotions should be speciﬁed. In regards to the re-

sults of cluster analysis of responses using an Affect

Grid, two clusters such as “Pleasant” and “Unpleas-

ant” feelings can be extracted. When factors con-

cerning the photo models were omitted, the same two

clusters were also extracted. Though the classes of

facial emotion are often discussed, typical emotions

can be easier to detect. Therefore, the structure of the

two clusters is an important scale for emotional im-

pressions. Also, this classiﬁcation can be applied to

impressions for any types of images, such as “Pleas-

ant” and “Unpleasant” images. The relationship be-

tween the two clusters of viewed facial emotions and

the features of ERPs has been conﬁrmed. The impres-

sion of the image can be estimated to be either of the

two clusters using just the features of ERPs.

Regarding chronological analysis of ERP wave-

forms, signiﬁcant differences in Fz and Cz ERPs can

be detected between the two clusters, but there is no

difference at Oz. The emotional recognition is one

of high level processing, and the differences appear

on potentials at the mid and frontal areas. There-

fore, more detailed information should be collected

from these areas of the scalp. Additionally, there is

no signiﬁcant difference in features of ERPs across

the three trials. The validity of the possibility of de-

tection has been conﬁrmed. To detect this activity,

the use of a selection of band pass ﬁlters directly af-

fects feature extraction. Analysis of the amplitude

and frequency components of ERPs conﬁrms that the

lower frequency of the band pass ﬁlter should be set

at around 2.0Hz.

The possibility of predicting viewer’s impressions

and dividing them into two clusters of facial emotion

has been examined. This means that viewer’s subjec-

tive impressions of an emotion can be estimated us-

ing their EEG information while viewing occurs. To

improve prediction performance, appropriate feature

extraction and an appropriate combination of proce-

dures should be determined. Also, the possibility of

predicting the viewer’s emotional condition will be

conﬁrmed using photos which exclude those which

display facial emotions.

5 CONCLUSION

In order to evaluate the viewer’s impression of the

emotion they are viewing, the relationship between

emotion impressions of images of facial expressions

and features of Event Related Potentials was ana-

lyzed. Following points were extracted during the

analysis.

1. Subjective impressions of the photos of facial im-

ages were measured using the Affect Grid, and

two clusters known as “Pleasant” and “Unpleas-

ant” clusters were extracted.

2. The time slots showed signiﬁcant differences in

ERP waveforms between the two clusters at the

Frontal (Fz) and Central (Cz) electrodes, except

at the Occipital electrode (Oz).

3. In comparing the freqeuncy power of the two time

slots, -100∼60 milliseconds (T1) and 60∼220

milliseconds (T2), the amplitudes of slot T2 are

larger. The differences in the amplitudes of slot

T2 between the two clusters for both Fz and Cz at

6.25Hz are remarkable.

4. Five band pass ﬁlters were used in this analysis.

The results of the analysis conﬁrms that the lower

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frequency of the band pass ﬁlter should be set at

around 2.0Hz.

Discriminant analysis was conducted using the

features of ERP, in order to evaluate the signiﬁcance

of these, and some signiﬁcant accuracy was obtained.

To generate more signiﬁcant features to measure the

viewer’s state of emotion while they are viewing im-

ages of facial expressions, some additional biosignals

such as eye movements using EOGs should be con-

sidered. They will be subject of our further study.

ACKNOWLEDGEMENT

This research was partially supported by the Japan

Society for the Promotion of Science (JSPS), Grant-

in-Aid for Scientiﬁc Research (B-26282046:2014-

2016).

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