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EEG databases for emotion recognition

 
: Liu, Yisi; Sourina, Olga

:

Mao, X. ; Institute of Electrical and Electronics Engineers -IEEE-:
International Conference on Cyberworlds, CW 2013. Proceedings : Yokohama, Japan, 21 - 23 October 2013
Los Alamitos, Calif.: IEEE Computer Society Conference Publishing Services (CPS), 2013
ISBN: 978-1-4799-2245-1 (Print)
ISBN: 978-1-4799-2247-5
pp.302-309
International Conference on Cyberworlds (CW) <12, 2013, Yokohama>
English
Conference Paper
Fraunhofer IDM@NTU ()
emotion recognition; affective computing; classification methods

Abstract
Emotion recognition from Electroencephalogram (EEG) rapidly gains interest from research community. Two affective EEG databases are presented in this paper. Two experiments are conducted to set up the databases. Audio and visual stimuli are used to evoke emotions during the experiments. The stimuli are selected from IADS and IAPS databases. 14 subjects participated in each experiment. Emotiv EEG device is used for the data recording. The EEG data are rated by the participants with arousal, valence, and dominance levels. The correlation between powers of different EEG bands and the affective ratings is studied.
The results agree with the literature findings and analyses of benchmark DEAP database that proves the reliability of the two databases. Similar brain patterns of emotions are obtained between the established databases and the benchmark database. A SVM-based emotion recognition algorithm is proposed and applied to both databases and the benchmark database. Use of a Fractal Dimension feature in combination with statistical and Higher Order Crossings (HOC) features gives us results with the best accuracy. Up to 8 emotions can be recognized. The accuracy is consistent between the established databases and the benchmark database.

: http://publica.fraunhofer.de/documents/N-294658.html