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Emotion classification from electroencephalogram using fuzzy support vector machine

Chatchinarat, A., Wong, K.W. and Fung, C.C. (2017) Emotion classification from electroencephalogram using fuzzy support vector machine. Lecture Notes in Computer Science, 10634 . pp. 455-462.

Link to Published Version: https://doi.org/10.1007/978-3-319-70087-8_48
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Abstract

Realization of human emotion classification from Electroencephalogram (EEG) has great potential. Various methods in machine learning have been applied for EEG emotion classification and among these techniques, Support Vector Machines (SVMs) has demonstrated that it can provide good classification results. Therefore, SVM has been used widely in Affective Brain-Computer Interfaces (aBCI). However, EEG signals are non-stationary and they normally associate with outliers and uncertainties, and these issues could affect the performance of SVM. This study proposes the use of Fuzzy Support Vector Machine (FSVM) to deal with these issues. A benchmark dataset, Database for Emotion Analysis using Physiological Signals (DEAP), was used for subject-dependence classification. The experimental results showed that FSVM could deal with uncertainties and outliers, and enhanced the accuracies of arousal, valence and dominance classifications when compared to the SVM. Moreover, it was found that when gamma band was used as a feature from the two channels gave the best performance in comparison to other bands.

Publication Type: Journal Article
Murdoch Affiliation: School of Engineering and Information Technology
Publisher: Springer Verlag
Copyright: © 2017 Springer International Publishing AG
URI: http://researchrepository.murdoch.edu.au/id/eprint/39823
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