An open software package to develop BCI based brain and cognitive computing technology for recognizing user's intention using deep learning

EEG Motor Imagery Tasks Classification (by Channels) via Convolutional Neural Networks (CNNs) based on TensorFlow

Attention temporal convolutional network for EEG-based motor imagery classification

CTNet: A Convolutional Transformer Network for EEG-Based Motor Imagery Classification

Source Code for "Adaptive Transfer Learning with Deep CNN for EEG Motor Imagery Classification".

Matlab source code of the paper "D. Wu, X. Jiang, R. Peng, W. Kong, J. Huang and Z. Zeng, Transfer Learning for Motor Imagery Based Brain-Computer Interfaces: A Complete Pipeline, Information Sciences, 2021, submitted."

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).

Manifold Embedded Knowledge Transfer for Brain-Computer Interfaces (MEKT)

Motor Imagery EEG Signal Classification Using Random Subspace Ensemble Network

Official code for "Attention-Based Spatio-Temporal-Spectral Feature Learning for Subject-Specific EEG Classification" paper

The decoding of continuous EEG rhythms during action observation (AO), motor imagery (MI), and motor execution (ME) for standing and sitting. (IEEE Sensors Journal)

Implementation of filter bank common spatial pattern (FBCSP) for MI-based BCI in python

A basic demonstration how to use Python, MNE, and PyTorch to analyze EEG signal.

Towards Domain Free Transformer for Generalized EEG Pre-training

EEG BCI Real-Time Applications: Contains real-time demonstrations of BCI applications

In AugmentBrain we investigate the performance of different data augmentation methods for the classification of Motor Imagery (MI) data using a Convolutional Neural Network tailored for EEG named EEGNet.

The codes that I implemented during my B.Sc. project.

A trusted repository for groundbreaking EEG research code. Some peer-reviewed algorithms (such as EEG data augmentation techniques, EEG classification models) to push the boundaries of neuroscience.

Project to test the accuracy of multiple algorithms published in articles to the EEG binary motor imagery problem