Graph adaptive semi-supervised discriminative subspace learning for EEG emotion recognition

Since Electroencephalogram (EEG) is resistant to camouflage and contains abundant neurophysiological information, it shows significant superiorities in objective emotion recognition, making EEG-based emotion recognition become a hot research field in brain-computer interface research. However, EEG is generally non-stationary and has a low signal-to-noise ratio, which is difficult to analyze. Inspired by the consensus that exploring a discriminative subspace representation usually helps to capture the semantic information of EEG data, in this paper we propose a Graph Adaptive Semi-supervised Discriminative Subspace Learning (GASDSL) model for EEG-based emotion recognition. GASDSL aims to explore a discriminative subspace in which the intra-class scatter decreases while the inter-class separability increases. The adaptive maximum entropy graph construction and semi-supervised subspace emotional state prediction are adopted to mediate the discriminative subspace learning. Extensive comparative studies on the SEED-IV and SEED-V datasets depict that 1) GASDSL achieved satisfactory emotion recognition accuracy compared with other semi-supervised learning models, 2) the discriminative abilities of both the learned maximum entropy graph and subspace are improved as the model iterates, and 3) the features extracted from the Gamma band, the left/right temporal, prefrontal, and (central) parietal lobes contributed more to emotion recognition based on the spatial-frequency pattern analysis results.