PAT is a unified training framework that jointly improves accuracy, robustness, and privacy in EEG decoding for BCIs under centralized source-free, federated source-free, and privacy-preserved source data transfer scenarios.
Random forests in non-invasive sensorimotor rhythm brain-comput er interfaces: a practical and convenient non-linear classifier
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PAT: Privacy-Preserving Adversarial Transfer for Accurate, Robust and Privacy-Preserving EEG Decoding
PAT is a unified training framework that jointly improves accuracy, robustness, and privacy in EEG decoding for BCIs under centralized source-free, federated source-free, and privacy-preserved source data transfer scenarios.