Enhancing EEG-Based Emotion Recognition Using MultiDomain Features and Genetic Algorithm Based Feature Selection

Electroencephalography (EEG) based emotion recognition has become a subtle research area because of its promising applications. An effective emotion recognition relies on significant and stable features. In this paper, we propose an EEG based emotion recognition methodology based on a hybrid feature extraction combined with Genetic Algorithm (GA) based feature selection. The features are extracted from three domains: time, frequency and discrete wavelet The proposal is evaluated on DEAP dataset where the emotional states are classified using a GA optimized Multi-Layer Perceptron. The proposed model identifies a. two classes of emotions viz. Low/High Valence with an average accuracy of 95.96 \(\%\) and Low/High Arousal with an average accuracy of 95.39 \(\%\) , b. four classes of emotions viz. High Valence-Low Arousal, High Valence-High Arousal, Low Valence-Low Arousal and Low Valence-High Arousal with 91.88 \(\%\) accuracy, which are better compared to the existing results reported in the literature.