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

No description provided.

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

EEG Motor Imagery Classification

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

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

Using Deep Learning techniques to classify Motor Imagery Electroencephalography (EEG) signals

Motor Imagery System Using a Low-Cost EEG Brain Computer Interface.

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 MATLAB toolbox for classification of motor imagery tasks in EEG-based BCI system with CSP, FB-CSP and BSSFO

EEG Classification API using Flask

This repository contains all the code used in the experiments of the paper Restricted Exhaustive Search for Frequency Band Selection in Motor Imagery Classification as well as additional information of the experiments and results, and how to reproduce them.

Train Once, Transfer Anywhere: Toward Device-Homogeneous MI-EEG Decoding

Real-Time BCI for Rock-Paper-Scissors: Decoding Motor Imagery with Minimal Training

University MS Thesis Project, Controlling an avatar in a Virtual Environment via EEG Motor Imagery

This project aim is to classify the motor imagery signals extracted from the brain using an Electro Encephalogram