CONSISTENCY-REGULARIZED MULTI-VIEW TRANSFER LEARNING FOR EPILEPTIC EEG RECOGNITION

Epilepsy is a neurological disorder characterized by transient cerebral dysfunction arising from abnormal cortical neuronal discharges, which can precipitate sudden muscle contractions, loss of consciousness, and convulsions. As one of the most prevalent brain disorders, it affects a substantial share of the global population. Electroencephalography (EEG) — the most widely used and extensively studied noninvasive modality for monitoring cerebral electrical activity — underpins clinical and computational approaches to epilepsy detection. In this work, we propose a Consistency-Regularized Multi-View Transfer Learning (CR-MVTL) algorithm for epileptic EEG analysis and AI-assisted detection. Compared with prevailing methods, CR-MVTL offers: (i) strong cross-view complementarity that promotes information sharing and improves recognition; (ii) high generalizability that enables rapid parameter adaptation when target-domain data are scarce; and (iii) dynamic view-weighting that attenuates weak views while amplifying informative ones. We evaluate CR-MVTL across 12 simulated multi-view transfer scenarios. Extensive experiments demonstrate absolute improvements in average accuracy of 7.25 and 6.41 percentage points in the two-view and three-view settings, respectively, over competitive baselines.