다중 브랜치 트랜스포머 기반 EEG 인지 상태 분류

초록

This study proposes a three-branch hybrid transformer model, termed EEG Branch Transformer (EBT), to improve cross-subject classification of cognitive fatigue and mental workload from electroencephalography (EEG) signals. Materials and Methods: EBT separately encodes temporal dynamics with a lightweight EEG-specific transformer, time–frequency representations with shared convolution and multi-scale Swin transformers, and spatial topology using 2D convolution on electrode grids, followed by learnable weighted feature fusion. The model was evaluated on three public datasets (Driving EEG dataset for fatigue, EEGMAT and STEW for mental workload) under a leaveone- subject-out cross-validation protocol. Performance was assessed using accuracy and macro F1-score. Compared with the EEG-Deformer baseline, EBT achieved higher accuracy and macro F1 on EEGMAT and STEW, indicating better cross-subject generalization for mental workload estimation. On the Driving EEG dataset, EEG-Deformer obtained slightly higher accuracy, whereas EBT showed comparable macro F1 and reduced inter-subject variability. These results suggest that balancing temporal, time–frequency, and spatial features through branch-wise transformers and weighted fusion is effective for robust EEG-based cognitive state classification across heterogeneous datasets and subjects.

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