Electroencephalogram-Based Emotion Recognition Using a Particle Swarm Optimization-Derived Support Vector Machine Classifier.

Abstract

We sort human emotions using Russell's circumplex model of emotion by classifying electroencephalogram (EEG) signals from 25 subjects into four discrete states, namely, happy, sad, angry, and relaxed. After acquiring signals, we use a standard database for emotion analysis using physiological EEG signals. Once raw signals are pre-processed in an EEGLAB, we perform feature extraction using Matrix Laboratory and apply discrete wavelet transform. Before classifying we optimize extracted features with particle swarm optimization. The acquired set of EEG signals are validated after finding average classification accuracy of 75.25%, average sensitivity of 76.8%, and average specificity of 91.06%.