[Old version] PyTorch implementation of EEGNet: A Compact Convolutional Network for EEG-based Brain-Computer Interfaces - https://arxiv.org/pdf/1611.08024.pdf
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Updated
Jul 10, 2019 - Jupyter Notebook
[Old version] PyTorch implementation of EEGNet: A Compact Convolutional Network for EEG-based Brain-Computer Interfaces - https://arxiv.org/pdf/1611.08024.pdf
Class to automatic create Convolutional Neural Network in PyTorch
Improving performance of motor imagery classification using variational-autoencoder and synthetic EEG signals
Project for XAI606(Korea University)
Stage training Implementation
Deep Learning pipeline for motor-imagery classification.
Labs for 5003 Deep Learning Practice course in summer term 2021 at NYCU.
The codes that I implemented during my B.Sc. project.
It is the task to classify BCI competition datasets (EEG signals) using EEGNet and DeepConvNet with different activation functions. You can get some detailed introduction and experimental results in the link below. https://github.com/secondlevel/EEG-classification/blob/main/Experiment%20Report.pdf
NCTU(NYCU) Deep Learning and Practice Spring 2021
EEG Artifact Removal Using Deep Learning (source code, IEEE Journal of Biomedical and Health Informatics)
Machine Learning based Brain Computer Interface (BCI) by analyzing EEG Data using PyTorch
NYCU Deep Learning and Practice Summer 2023
EEG Classification API using Flask
This code implements the EEG Net deep learning model using PyTorch. The EEG Net model is based on the research paper titled "EEGNet: A Compact Convolutional Neural Network for EEG-based Brain-Computer Interfaces".
PyTorch code for "Motor Imagery Decoding Using Ensemble Curriculum Learning and Collaborative Training"
Processing EEG data using Speechbrain-MOABB and model tuning to get best results
NYU CS-GY 9223 E Neuroinformatics (Spring 2024) - Final Project
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