Emotion estimation from EEG signals using wavelet transform analysis

Emotion recognition is important for an effective human-machine interaction. Information obtained from speech, gestures and mimics, heart rate, and temperature can be used in emotion estimation. In this study, emotion estimation from EEG signals using wavelet decomposition is performed. For this purpose, EEG signals were recorded from 20 subjects and audio stimuli are used to evoke emotions. Delta, Theta, Alfa, Beta and Gamma sub-bands of signals are computed using wavelet transform. Statistical features and energy of each band are computed. Correlation based feature selection algorithm is applied to the base feature set to obtain the most relevant subset and emotion primitives are estimated using Support Vector Regression. Emotion estimation results in terms of mean absolute error using db4, db8 and coif5 mother wavelets are 0.28, 0.26, and 0.29 for valence, 0.20, 0.20, and 0.19 for activation and 0.11, 0.10, and 0.10 for dominance respectively.