High-gamma dataset described in Schirrmeister et al. 2017
A high-gamma EEG dataset comprising 14 healthy subjects performing motor imagery tasks (left hand, right hand, feet, and rest) recorded at 500 Hz with 128 channels. This is a BIDS-formatted derivative of the original dataset described in Schirrmeister et al. 2017, which was used to develop and validate deep convolutional neural networks for end-to-end EEG decoding. The derivative demonstrates that deep learning approaches can match or exceed traditional feature-based methods (FBCSP) while learning interpretable spectral power modulations in alpha, beta, and high-gamma frequency bands.
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Coming soon. Per-file data-quality summaries are precomputed by the NEMAR processing pipeline. The static aggregate is on the way — tracked at nemar-cli#511.