Emotion recognition can be used in clinical and nonclinical situations. Despite previous works which mostly used time and frequency features of electroencephalogram (EEG) signals in subject-dependent emotion recognition issues, we used multiscale fuzzy entropy as a nonlinear dynamic feature. The EEG signals of the well-known Database for Emotion Analysis Using Physiological signals dataset was used for classification of two and three levels of emotions in arousal and valence space. The compound feature selection with a cost of average accuracy of support vector machine classifier was used to reduce feature dimensions. For subject-dependent systems, the proposed method is superior in comparison to previous works with 90.81 % and 90.53 % accuracies in two-level classification and 79.83 % and 77.80 % accuracies in three-level classification in arousal and valence dimensions, respectively.
Emotion recognition, multiscale fuzzy entropy, electroencephalogram, support vector machine
LOTFALINEZHAD, HAMZAH and MALEKI, ALI
"Application of multiscale fuzzy entropy features for multilevel subject-dependent emotion recognition,"
Turkish Journal of Electrical Engineering and Computer Sciences: Vol. 27:
6, Article 4.
Available at: https://journals.tubitak.gov.tr/elektrik/vol27/iss6/4