A Deep Learning library for EEG Tasks (Signals) Classification, based on TensorFlow.
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Updated
Jul 20, 2025 - 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
Deep Learning pipeline for motor-imagery classification.
Rethinking CNN Architecture for Enhancing Decoding Performance of Motor Imagery-based EEG Signals
Towards Domain Free Transformer for Generalized EEG Pre-training
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.
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.
Implementation of Convolutional Recurrent Neural Network (CRNN) to decode motor imagery EEG data.
A Novel Adversarial Approach for EEG Dataset Refinement: Enhancing Generalization through Proximity-to-Boundary Scoring
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..
EEG Motor Imagery Classification
EEG Classification API using Flask
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.
Real-Time BCI for Rock-Paper-Scissors: Decoding Motor Imagery with Minimal Training
University MS Thesis Project, Controlling an avatar in a Virtual Environment via EEG Motor Imagery
Train Once, Transfer Anywhere: Toward Device-Homogeneous MI-EEG Decoding
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.
Exploring Brain Signal Processing Pipelines for Kaggle Challenges
Project for XAI606(Korea University)
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