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.
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A survey that taxonomizes synthetic brain signal generation methods into four categories, benchmarks them on motor imagery, seizure detection, SSVEP, and auditory attention tasks, and outlines evaluation principles and future directions for data-efficient BCIs.
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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.
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Synthetic Data Generation for Brain-Computer Interfaces: Overview, Benchmarking, and Future Directions
A survey that taxonomizes synthetic brain signal generation methods into four categories, benchmarks them on motor imagery, seizure detection, SSVEP, and auditory attention tasks, and outlines evaluation principles and future directions for data-efficient BCIs.