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Emotion recognition from EEG signals using back propagation neural network

The proposed work presents a discrete wavelet based feature extraction method in conjunction with a back propagation neural network (BPNN) for the classification of emotions from EEG recordings. The EEG recordings were used from DEAP dataset (A Database for emotion analysis using physiological signals) for evaluation of the method. Russell’s model was used for quantification of emotions in valence and arousal dimension space. The emotions were classified into four classes, namely – high arousal positive valence (HAPV), high arousal negative valence (HANV), low arousal positive valence (LAPV) and low arousal negative valence (LANV). We have used three mother wavelets Sym8, Coif5 and Db8 for extracting four features, namely entropy, mean, standard deviation and variance. Classification of the emotions was done using BPNN. An average classification accuracy of 57.9% was obtained in the classification of the above mentioned four classes using different mother wavelets.