University MS Thesis Project, Controlling an avatar in a Virtual Environment via EEG Motor Imagery
-
Updated
Jun 25, 2024 - Python
University MS Thesis Project, Controlling an avatar in a Virtual Environment via EEG Motor Imagery
Attention temporal convolutional network for EEG-based motor imagery classification
A trusted repository for groundbreaking EEG research code. Some peer-reviewed algorithms (such as EEG data augmentation techniques, EEG classification models) to push the boundaries of neuroscience.
EEG Motor Imagery Tasks Classification (by Channels) via Convolutional Neural Networks (CNNs) based on TensorFlow
Deep Learning toolbox for EEG based Brain-Computer Interface signals decoding and benchmarking
The decoding of continuous EEG rhythms during action observation (AO), motor imagery (MI), and motor execution (ME) for standing and sitting. (IEEE Sensors Journal)
PyTorch code for "Motor Imagery Decoding Using Ensemble Curriculum Learning and Collaborative Training"
Towards Domain Free Transformer for Generalized EEG Pre-training
Neuroexon presents a hybrid-BCI system that utilizes motor imagery (MI) and steady-state visual-evoked potential (SSVEP) to control a one degree of freedom arm exoskeleton which provides the user with haptic feedback.
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.
Implementation of Convolutional Recurrent Neural Network (CRNN) to decode motor imagery EEG data.
Source Code for "Adaptive Transfer Learning with Deep CNN for EEG Motor Imagery Classification".
The codes that I implemented during my B.Sc. project.
Official code for "Attention-Based Spatio-Temporal-Spectral Feature Learning for Subject-Specific EEG Classification" paper
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.
A display for training phase for motor imagery-based BCI (MI-based BCI) training using pygame.
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.
Add a description, image, and links to the motor-imagery topic page so that developers can more easily learn about it.
To associate your repository with the motor-imagery topic, visit your repo's landing page and select "manage topics."