To effectively identify electroencephalogram (EEG) signals in multiple-source domains, a multiple-source transfer learning-based Takagi–Sugeno–Kang (TSK) fuzzy system (FS), called MST-TSK, is proposed, which combines multiple-source transfer learning and manifold regularization (MR) learning mechanisms together into the TSK-FS framework. Specifically, the advantages of MST-TSK include the following: (1) by evaluating the significance of each source domain (SD), a flexible domain entropy-weighting index is presented; (2) using the theory of sample transfer learning, a reweighting strategy is presented to weigh the prediction of unknown samples in the target domain (TD) and the output of the source prediction functions; (3) by taking into account the MR term, the manifold structure of the TD is effectively maintained in the proposed system; and (4) by inheriting the interpretability of TSK-FS, MST-TSK displays good interpretability in identifying EEG signals that are understandable by humans (domain experts). The effectiveness of the proposed FS is demonstrated in several EEG multiple-source transfer learning tasks.

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