A Deep Learning library for EEG Tasks (Signals) Classification, based on TensorFlow.
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Updated
Jan 19, 2023 - Python
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
IEEE Transactions on Emerging Topics in Computational Intelligence
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).
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
Motor Imagery EEG signal Classification on DWT
EEG Motor Imagery Classification Using CNN, Transformer, and MLP
Towards Domain Free Transformer for Generalized EEG Pre-training
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.
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..
Using Deep Learning techniques to classify Motor Imagery Electroencephalography (EEG) signals
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.
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
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