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).

Processing EEG data using Speechbrain-MOABB and model tuning to get best results

EEG classification project. Uses a variety of classifiers and methods to parse EEG data.

This example compares the classification performance of linear support vector machine (LinearSVC) on the Riemannian Transfer Learning method (RPA, Rodrigues et al., 2018) and the golden-standard subject-wise train-test cross-validation method using real P300 BCI data.

EEG Classification using MiniRocket + Ridge Classifier on BCI Competition IV Dataset 2a. Reproducible pipeline for Time-Series feature extraction and classification.

The Neural Interface Foundation Challenge — NeurIPS 2026 Sydney. An open-source brain-decoding benchmark with four tracks across EEG, EMG, sleep, and BCI.

Motor-imagery EEG decoding with covariance alignment (EA/Riemannian) and information-maximization test-time adaptation on an EEGNet backbone — benchmarked on BCI Competition IV-2a & IV-2b (within-subject and cross-subject).