JET is a conditional flow matching framework that generates EEG as continuous raw sequences with added constraints for spectral and temporal properties, achieving over 40% lower TS-FID than prior discrete denoising methods on three benchmarks.
MEG-GPT: A transformer-based foundation model for magnetoencephal ography data,
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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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Let EEG Models Learn EEG
JET is a conditional flow matching framework that generates EEG as continuous raw sequences with added constraints for spectral and temporal properties, achieving over 40% lower TS-FID than prior discrete denoising methods on three benchmarks.
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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.