Classification of EEG correlates on emotion using features from Gaussian mixtures of EEG spectrogram

Abstract

This paper presents the classification of EEG correlates on emotion using features extracted by Gaussian mixtures of EEG spectrogram. This method is compared with three feature extraction methods based on fractal dimension of EEG signal including Higuchi, Minkowski Bouligand, and Fractional Brownian motion. The K nearest neighbor and Support Vector Machine are applied to classify extracted features. The 4 emotional states investigated in this paper are defined using the valence-arousal plane: two valence states (positive and negative) and two arousal states (calm, excited). The accuracy of system to classify 4 emotional states is investigated on EEG collected from 26 subjects (20 to 32 years old) while exposed to emotionally-related visual and audio stimuli. The results showed that the proposed feature extraction using Gaussian mixtures of EEG spectrogram yielded better classification results using the KNN classifier.

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