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
EEG Motor Imagery Classification Using CNN, Transformer, and MLP
Rethinking CNN Architecture for Enhancing Decoding Performance of Motor Imagery-based EEG Signals
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
Motor Imagery System Using a Low-Cost EEG Brain Computer Interface.
A Novel Adversarial Approach for EEG Dataset Refinement: Enhancing Generalization through Proximity-to-Boundary Scoring
Implementation of Convolutional Recurrent Neural Network (CRNN) to decode motor imagery EEG data.