A Multi-view Semi-supervised Takagi–Sugeno–Kang Fuzzy System for EEG Emotion Classification

Electroencephalogram (EEG)-based emotion recognition plays an important role in brain-computer interface and mental health monitoring. The large amount of EEG data but the lacks of labeling, multi-feature attribute, and data uncertainty are the difficulties in its recognition problem. A multi-view semi-supervised Takagi–Sugeno–Kang (MV-SS-TSK) fuzzy system is developed for EEG emotion classification in this paper. In the learning of fuzzy system consequent, firstly, a novel joint learning of semi-supervised learning, sparse representation, and low-rank coding is developed for semi-supervised sparse consequent factor learning, which makes the consequent parameter learning as a pseudo-label-only optimization problem. In particular, to simplify fuzzy rules, the sparse constraint term ensures the consequent parameters to be sparse in rows. Secondly, the consequent factor learning in a single feature view is extended into the multi-view learning model. In particular, low-rank coding is considered in multi-view semi-supervised consequent parameter learning. The low-rank constraint on view-shared component of consequent factor is implemented to exploit global data structure. The sparse constraint on view-dependent component of consequent factor is implemented to retain the feature diversity representation. By minimizing the intersection between view-shared component and view-specific components for different views, MV-SS-TSK can take advantage of the intrinsic relationship between various features and capture the consistency from multi-view features. Experiments on the SEED dataset show the superior performance of the proposed fuzzy system.