EPECT: An Eigenvalue-Guided Positional Encoding Classification Transformer for Cross-Subject EEG-fNIRS Decoding
EPECT combines convolution, positional encoding and eigenvalue-guided attention to classify motor-imagery and cognitive tasks from EEG-fNIRS. Across two public datasets under leave-one-subject-out evaluation, reported accuracies ranged from 96.3% to 98.1%, with ablation and integrated-gradients analyses. These were research datasets, not real rehabilitation patients.