A research repository of deep learning on electroencephalographic (EEG) for Motor imagery(MI), including eeg data processing(visualization & analysis), papers(research and summary), deep learning models(reproduction and experiments).
The Neural Interface Foundation Challenge — NeurIPS 2026 Sydney. An open-source brain-decoding benchmark with four tracks across EEG, EMG, sleep, and BCI.
Predicting motor imagery BCI performance from EEG signal quality features, a cross-dataset study comparing CSP+LDA and EEGNet.
Applying REVE, an EEG foundation model, to seizure detection using the CHB-MIT scalp EEG dataset.
Reproducible cross-session four-class motor imagery EEG baseline with Braindecode ShallowFBCSPNet
Leakage-resistant cross-subject EEG experiments comparing EEGNet with pretrained LaBraM on PhysioNet.
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