Swap-adversarial learning with inter-subject channel swapping is claimed to improve cross-subject, cross-device, and cross-dataset PD classification, but the supporting evidence is limited by small samples, per-setting tuning, and a time-confounded benchmark.
Parkinson's disease detection from resting state EEG using multi-head graph structure learning with gradient weighted graph attention explanations
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A swap-adversarial framework for improving domain generalization in electrocorticography-based Parkinson's disease classification
Swap-adversarial learning with inter-subject channel swapping is claimed to improve cross-subject, cross-device, and cross-dataset PD classification, but the supporting evidence is limited by small samples, per-setting tuning, and a time-confounded benchmark.