Multiview Transfer Fuzzy Classification With Soft-Variable Embedded and Discriminative Structure Preservation on Motor Imagery Electroencephalogram

To address the challenges of high uncertainty, inter-subject variability, and inefficiency multi-feature utilization in motor imagery electroencephalogram (MI-EEG) classification, this study proposes amultiviewtransferTakagi-Sugeno-Kang (TSK) fuzzy classifier withsoftvariable embedded anddiscriminativestructural preservation (MVT-TSK-SVDS). First, a transfer learning mechanism incorporating soft variable embedding in the consequent part is developed. This mechanism establishes cross-domain correlations via a shared consequent and representation matrix. Within this framework, soft variable embedding and low-rank constrained discriminative learning work in concert to effectively capture supervision information and cross-domain relationships. Second, a local-global structural preservation term incorporating graph embedding and low-rank constraint is implemented to maintain local discriminative information from source domain while integrating global geometric patterns across all data. Third, a multiview adaptive learning framework is designed to address feature representation diversity and information loss during knowledge transfer. MVT-TSK-SVDS dynamically optimizes view-specific contributions through entropy maximization criterion while ensuring collaborative decision via consistency constraints. Experimental results validate strong generalization between and across datasets. Our model achieves 62.16% and 72.71% accuracy in cross-subject tasks on BCI-IV 2a and OpenBMI, respectively. In cross-dataset evaluations, it attains 62.75% accuracy on BCI-IV 2a to OpenBMI and 65.08% accuracy on OpenBMI to BCI-IV 2a, respectively.