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arl-eegmodels

This is the Army Research Laboratory (ARL) EEGModels Project: A Collection of Convolutional Neural Network (CNN) models for EEG signal classification, using Keras and Tensorflow

augmentbrain

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.

bci_mi_wavelet_cnn

Using wavelet transform to extract time-frequency features of motor imagery EEG signals, and classify it by convolutional neural network

bcidatasetiv2a

This is a repository for BCI Competition 2008 dataset IV 2a fixed and optimized for python and numpy. This dataset is related with motor imagery

cnn-mi-bci

CNN-SAE program for MI-BCI classification. (Based on "Tabar et al-2016-J Neural Eng. A novel deep learning approach for classification of EEG motor imagery signals")

eeg-adapt

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

eeg-analysis-for-motor-imagery-application

eeg-data-augmentation-using-variational-autoencoder

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

eeg-dl

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

eeg-motor-imagery

ECE-GY 9123 Project: GCN-Explain-Net: An Explainable Graph Convolutional Neural Network (GCN) for EEG-based Motor Imagery Classification and Demystification

eeg-motor-imagery-classification-cnns-tensorflow

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

eeg-transformer

i. A practical application of Transformer (ViT) on 2-D physiological signal (EEG) classification tasks. Also could be tried with EMG, EOG, ECG, etc. ii. Including the attention of spatial dimension (channel attention) and *temporal dimension*. iii. Common spatial pattern (CSP), an efficient feature enhancement method, realized with Python.

eegnet

EEGNet remodel performed within internship at Swartz Center for Computational Neuroscience

icassp2020-2020-code

iv-2a

motor-imagery

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

motor-imagery-deep-learning

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