Emotion recognition based on physiological signals using brain asymmetry index and echo state network

This paper proposes a method to evaluate the degree of emotion being motivated in continuous music videos based on asymmetry index (AsI). By collecting two groups of electroencephalogram (EEG) signals from 6 channels (Fp1, Fp2, Fz and AF3, AF4, Fz) in the left and right hemispheres, multidimensional directed information is used to measure the mutual information shared between two frontal lobes, and then, we get AsI to estimate the degree of emotional induction. In order to evaluate the effect of AsI processing on physiological emotion recognition, 32-channel EEG signals, 2-channel EEG signals and 2-channel EMG signals are selected for each subject from the DEAP dataset, and different sub-bands are extracted using wavelet packet transform. k-means algorithm is used to cluster the wavelet packet coefficients of each sub-band, and the probability distribution of the coefficients under each cluster is calculated. Finally, the probability distribution value of each sample is sent as the original features into echo state network for unsupervised intrinsic plasticity training; the reservoir state nodes are selected as the final feature vector and fed into the support vector machine. The experimental results show that the proposed algorithm can achieve an average recognition rate of 70.5% when the subjects are independent. Compared with the case without AsI, the recognition rate is increased by 8.73%. On the other hand, the ESN is adopted for the original physiological feature refinement which can significantly reduce feature dimensions and be more beneficial to the emotion classification. Therefore, this study can effectively improve the performance of human–machine interface systems based on emotion recognition.