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

Deep Learning pipeline for motor-imagery classification.

Rethinking CNN Architecture for Enhancing Decoding Performance of Motor Imagery-based EEG Signals

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.

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.

Towards Domain Free Transformer for Generalized EEG Pre-training

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..

Project for XAI606(Korea University)

EEG Classification API using Flask

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

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.

Exploring Brain Signal Processing Pipelines for Kaggle Challenges

Motor Imagery model for Technology Workshop class

This Python script creates, trains, and tests a Convolutional Neural Network (CNN) for image classification using various libraries like Numpy, Tensorflow, OpenCV, Keras, etc. The input images are spectrum images that are loaded from a specified folder path and pre-processed by resizing and normalizing.

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

The topic is "Using Different algorithm to classify the motor imagery EEG signal"