A swap-adversarial framework for improving domain generalization in electrocorticography-based Parkinson's disease classification

arXiv:2602.10528v2 Announce Type: replace Abstract: We propose a novel swap-adversarial framework that mitigates high inter-subject variability and the high-dimensional low-sample-size problem in electrocorticography (ECoG) data. It achieves robust domain generalization across ECoG and electroencephalography (EEG)-based brain-computer interface datasets. Our framework integrates (1) robust preprocessing, (2) inter-subject balanced channel swap (ISBCS) for cross-subject augmentation, and (3) domain-adversarial learning (DAL) to suppress subject-specific bias. The ISBCS method is a bio-inspired

Source: arXiv cs.LG — read the full report at the original publisher.

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