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.

Applying REVE, an EEG foundation model, to seizure detection using the CHB-MIT scalp EEG dataset.

Predicting motor imagery BCI performance from EEG signal quality features, a cross-dataset study comparing CSP+LDA and EEGNet.

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.