EEG-based emotion recognition from during
paying attention to prosodic information
Haruko Yagura, Hiroki Tanaka, Satoshi Nakamura
Augmented Human Communication Laboratory, Graduate School of Information Science, Nara Institute of Science and Technology in japan ( NAIST )BackgroundResultClassification Accuracy of γ (35-45Hz) amplitude• 0.10 - 0.15 s

EarlyGamma
Responsewith
Evoked • Phase-locked
stimuli-characteristic
Gamma • Working memory▶0.1 - 0.3 s▶40 Hz• 0.15 - 0.30 s• Changed by theemotional arousal on
the stimuli visual orauditory presented.

InducedGamma

Arousalemotionalclassificationmodel
during voice

Materials and Methods
Sound stimuli
Emotional
Intonation

ChanceRate

S10.82

0.76

S20.700.50

S30.700.66

Accuracy

ChanceRate

S10.850.76

S20.730.50

S30.76

0.66*Evoked gamma: 0.10-0.15 s, Induced gamma:0.15-0.30 s*Top of PCA`s components(n = 5) were selected for classification model.
*Classes labels were two which were divided into high (SAM value :5-9) and low (SAM value:1-4) arousal.
・Number of Low and High values; All = 30;S1(S1;high-17:low-13)-S2(high-20;low-10)-S3(high-15;low-15)

A first nameconsisting of threemorae. The suffix“ /sa・n/” whichmeans ‘Mr.or.Mrs’
in Japanese,consisting of twomorae.
 Time duration is0.74 ±0.2 s( n=30 ), and Sound
pressure label
were identical.

Sad (n = 10)
Neutral(n=10)

Emotional Tone
is neutral.

Stimuli sequences
PositiveAccuracyJoy (n =10 )/a・ra・i・sa・ｎ/

Induced Gamma

Evoked Gamma

Negative

Confusion Matrices

NeutralFig.01 Stimuli sequences

 ISI(inter stimulus interval) is 10s.
 10 sounds for Each emotion block which is sad. Joy and neutral emotionalsound.

EEG data acquisition
 Subjects; n = 4 ( age; mean = 24.7) One subject was excluded for remarkablenoise.
 EEG; Brain Vision acticap,32CH (Re-referenced on FCz ) Sampling: 1000Hz, hpf-0.01Hz lpf-60Hz, Artifact rejection with ICA

Subjective evaluation of emotional arousal
 Subjective evaluation was
done after the experiment by
Self-Assessment Manikin(SAM).
 This values were used for theclass label of the classification
model whether it is high(5-9)
or low arousal(1-4).

Fig.02 Self-Assessment Manikin (SAM)

Classification procedure with Machine learning

Discussion and Conclusion
 Accuracy of all subjects have exceeded the chance level.
 All subjects has no abnormality of accuracy from Confusion Matrices.
 All of the subjects had a higher accuracy to amplitude of induce gamma thanevoked gamma. Whether or not it reflects cognitive function requires more
scrutiny by increasing the number of subjects.
 It will be useful for developing communication support tools and devices that realtime detect his/her changes in physiological state emotions through the voice.

ReferencesEEG signal
preprocessing• FFT (35-45 Hz)• Evoked gamma (100-1150ms)• Induced gamma(150-300ms)• 32 channels• The EEG Data epoched withsingle triall

Feature extraction

Classification• 31 features ( = number ofchannels )• Data standardization• Dimensionally Reduction by
PCA(n=5)• Linear SVM, leave-one-out
cross-validation• Subject dependent model• Dimensionally Reduction by
PCA(Top component )• 2classes arousal model; high(SAM:5-9) and low (SAM:1-4)



Andreas Keil, et all, Effects of emotional arousal in the cerebralhemispheres: a study of oscillatory brain activity and event-related
potentials, Clinical Neurophysiology 112 (2001)
Andreas Keil, et al. Adaptation in human visual cortex as a mechanism
for rapid discrimination of aversive stimuli. NeuroImag,2007
Mahdiyeh S.,R. et al, Emotional Face Recognition in Children With
Attention Deficit/Hyperactivity Disorder: Evidence From Event Related
Gamma Oscillation, basic and clinical neuroscience, 2017.
Mu Li et al, Emotion Classification Based on Gamma-band EEG, IEEE,EMBC, 2007

Annual Meeting ofthe Japan
Neuroscience
SocietyDisclosure ofConflict of Interest
Name of firstauthor:Haruko Yagura
I have no COI
with regard to thepresentation.