A swap-adversarial framework for domain generalization in pre- and post-stimulation Parkinsonian state classification
Read the original on arXiv AI →The paper introduces a swap‑adversarial framework designed to address inter‑subject variability and the high‑dimensional low‑sample‑size challenge in electrocorticography (ECoG) data. It combines robust preprocessing, an inter‑subject balanced channel swap (ISBCS) for cross‑subject augmentation, and domain‑adversarial learning (DAL) to suppress subject‑specific bias, enabling robust domain generalization across both ECoG and EEG‑based brain‑computer interface datasets. The authors also present a new reproducible ECoG benchmark derived from long‑term recordings of 6‑hydroxydopamine‑induced rat models, and demonstrate the framework’s superior performance in cross‑subject, cross‑session, and cross‑dataset experiments.
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