Autors: Velchev, Y. S., Radeva S., Sokolov S., Radev D.
Title: Automated estimation of human emotion from EEG using statistical features and SVM
Keywords: EEG, Human Emotions, SVM

Abstract: This paper presents an approach for automated estimation of human emotions from electroencephalogram data. The used features are principally the Hjorth parameters calculated for theta, alpha, beta and gamma bands taken from certain channels. The classification stage is support vector machine. Since the human emotions are modelled as combinations from physiological elements such as arousal, valence, dominance, liking, etc., these quantities are the classifier's outputs. The best achieved correct classification performance is about 80%.

References

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    Copyright IEEE

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    Вид: публикация в международен форум, публикация в реферирано издание, индексирана в Scopus и Web of Science