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.

中文翻译：

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智能诊断

为了有效识别多源域中的脑电图（EEG）信号，提出了一种基于多源迁移学习的Takagi-Sugeno-Kang（TSK）模糊系统（FS），称为MST-TSK，它结合了多源迁移学习和流形正则化 (MR) 学习机制一起集成到 TSK-FS 框架中。具体来说，MST-TSK的优点包括：（1）通过评估每个源域（SD）的重要性，提出了一个灵活的域熵加权指数；(2)利用样本迁移学习的理论，提出了一种重加权策略，对目标域(TD)中未知样本的预测和源预测函数的输出进行加权；(3) 通过考虑 MR 项，TD 的流形结构在所提出的系统中得到有效维护；（4）通过继承TSK-FS的可解释性，MST-TSK在识别人类（领域专家）可以理解的EEG信号方面表现出良好的可解释性。所提出的 FS 的有效性在几个 EEG 多源迁移学习任务中得到了证明。