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.
Self-su pervised EEG denoising via dual-branch consistency learning with ma sked reconstruction,
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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.