Attention temporal convolutional network for EEG-based motor imagery classification

Mother of All BCI Benchmarks

EEG Preprocess and Microstate feature extraction for .edf/.bdf files.

classification of Motor Imagery signals using phase space and Poincare sections

This repository includes project work of my master's thesis.

Improving Motor Imagery EEG Classification by CNN with Data Augmentation

Code accompanying The Promise of Deep Learning for BCIs: Classification of Motor Imagery EEG using Convolutional Neural Network

The collection of representative deep learning-based MI-EEG models

Public EEG-based motor imagery (MI) datasets

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 E…

Motor execution (ME)/motor imagery (MI) cross-task adaptive transfer learning algorithm for MI EEG decoding

Classification algorithm based on motor imagery brain-computer interface

2021 ACMMM: Auto-MSFNet: Search Multi-scale Fusion Network for Salient Object Detection

Dual-Branch Convolution Network with Efficient Channel Attention for EEG-Based Motor Imagery Classification

Improving BCIs with generative models synthesizing realistic EEG signals. Co-authored research paper: https://arxiv.org/abs/2402.09453

code for NeurIPS_competition

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

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

Efficient Transfer Learning with Meta Update for Cross Subject EEG Classification

GAN and VAE implementations to generate artificial EEG data to improve motor imagery classification. Data based on BCI Competition IV, datasets 2a. Final project for UCLA's EE C247: Neural Networks…

Implementation of Deep Neural Networks in Keras and Tensorflow to classify motor imagery tasks using EEG data

Solution for EEG Classification via Multiscale Convolutional Net coded for NeuroHack at Yandex.

Graph Convolutional Networks for 4-class EEG Classification

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

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

脑电实验室用于研究运动想象的代码和历年脑电数据

This is an approach to reduce the number of Relevant Electrode for MI-BCI Prediction ( Using SVM as a Classifier )

Using combination EA (Euclidean Alignment) and TCA (Transfer Component Analysis) for transfer learning approach for MI-based BCI. This work served as a research project for master's degree completion.