EEG-based emotion recognition with manifold regularized extreme learning machine

EEG signals, which can record the electrical activity along the scalp, provide researchers a reliable channel for investigating human emotional states. In this paper, a new algorithm, manifold regularized extreme learning machine (MRELM), is proposed for recognizing human emotional states (positive, neutral and negative) from EEG data, which were previously evoked by watching different types of movie clips. The MRELM can simultaneously consider the geometrical structure and discriminative information in EEG data. Using differential entropy features across whole five frequency bands, the average accuracy of MRELM is 81.01%, which is better than those obtained by GELM (80.25%) and SVM (76.62%). The accuracies obtained from high frequency band features (β, γ) are obviously superior to those of low frequency band features, which shows β and γ bands are more relevant to emotional states transition. Moreover, experiments are conducted to further evaluate the efficacy of MRELM, where the training and test sets are from different sessions. The results demonstrate that the proposed MRELM is a competitive model for EEG-based emotion recognition.