Style transfer mapping with ensemble learning for cross-subject EEG-based neuropsychiatric disease recognition

Cross-subject electroencephalogram (EEG) recognition remains a fundamental challenge because large inter-subject variability causes severe distribution shifts that substantially degrade the generalization ability of existing learning models. Most existing transfer learning methods attempt to align global feature distributions but often overlook subject-specific structural variations, resulting in unstable adaptation under limited calibration data. To address this problem, we propose a Prototype-Consistent Style Transfer Mapping (PC-STM) framework for cross-subject neuropsychiatric disease recognition. Unlike conventional domain adaptation methods, the proposed framework models subject discrepancy as a prototype-guided style transformation that explicitly aligns target representations with class-consistent source prototypes while preserving discriminative structures. Specifically, a multi-source prototype construction strategy is introduced to characterize representative class distributions, followed by a confidence-aware transductive semi-supervised style transfer module that iteratively refines transformation parameters using labeled calibration samples and unlabeled evaluation samples. Furthermore, a reliability-aware ensemble learning strategy dynamically aggregates multiple subject-specific classifiers according to their calibration consistency, thereby improving robustness against heterogeneous subject distributions. Experiments on two public EEG datasets covering major depressive disorder and Parkinson's disease show stable performance under the evaluated leave-one-pair-out protocol. The calibration labels are inherited from the public dataset cohorts or task conditions, and the study does not constitute prospective clinical diagnostic validation. Beyond the evaluated EEG tasks, prototype-guided style transfer may provide a basis for future investigation of other limited-supervision recognition problems.