A Deep Learning library for EEG Tasks (Signals) Classification, based on TensorFlow.

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

Multi-Scale Convolutional Transformer Network for Motor Imagery Brain-Computer Interface

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).

IEEE Transactions on Emerging Topics in Computational Intelligence

Deep Learning pipeline for motor-imagery classification.

Improving performance of motor imagery classification using variational-autoencoder and synthetic EEG signals

Rethinking CNN Architecture for Enhancing Decoding Performance of Motor Imagery-based EEG Signals

EEG Motor Imagery Classification Using CNN, Transformer, and MLP

Towards Domain Free Transformer for Generalized EEG Pre-training

Motor Imagery EEG signal Classification on DWT

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

This is a python code for extracting EEG signals from dataset 2b from competition iv, then it converts the data to spectrogram images to classify them using a CNN classifier.

Record EEG data from a Muse 2 headband using the MInd Monitor app and python osc module. Build and train a CNN model in Keras framework to classify Left-Right Motor Imagery. Make real-time predictions using the trained model.

This is works in attempt to develop novel, state-of-the-art models for decoding EEG MI data from patient datasets. Specifically using GAT, highlighting their potential advantages.

Senior Design Project at UH

Implementation of Convolutional Recurrent Neural Network (CRNN) to decode motor imagery EEG data.

This code is for classifying spectrogram images of Motor Movement/Imagery tasks using a Convolutional Neural Network (CNN) and Generative Adversarial Network (GAN) for data augmentation..

A Novel Adversarial Approach for EEG Dataset Refinement: Enhancing Generalization through Proximity-to-Boundary Scoring