EEG-based emotion recognition based on kernel Fisher's discriminant analysis and spectral powers

In this paper, a feature extraction method called kernel Fisher's emotion pattern (KFEP) based on the kernel Fisher's discriminant analysis and spectral powers of multiple EEG rhythms is proposed for emotion recognition. An emotion-induction paradigm is designed for emotional EEG data coll...

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Bibliographic Details
Published in2014 IEEE International Conference on Systems, Man, and Cybernetics (SMC) pp. 2221 - 2225
Main Authors Yi-Hung Liu, Wei-Teng Cheng, Yu-Tsung Hsiao, Chien-Te Wu, Mu-Der Jeng
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.10.2014
Subjects
Brain modeling
brain-computer interface
EEG
Electroencephalography
Emotion recognition
Feature extraction
Kernel
kernel Fisher's discriminant analysis
Principal component analysis
Vectors
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Summary:In this paper, a feature extraction method called kernel Fisher's emotion pattern (KFEP) based on the kernel Fisher's discriminant analysis and spectral powers of multiple EEG rhythms is proposed for emotion recognition. An emotion-induction paradigm is designed for emotional EEG data collection, where a set of pictures selected from the International Affective Picture System (IAPS) are used as the emotion induction stimuli. Experimental results indicate that the KFEP feature performs better than the commonly used spectral power features. Our proposed KFEP achieves high classification accuracies of valence (78.49%) and arousal (81.93%).
ISSN:1062-922X
2577-1655
DOI:10.1109/SMC.2014.6974254