[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
Deep Learning pipeline for motor-imagery classification.
Improving performance of motor imagery classification using variational-autoencoder and synthetic EEG signals
EEG Artifact Removal Using Deep Learning (source code, IEEE Journal of Biomedical and Health Informatics)
The codes that I implemented during my B.Sc. project.
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".
Class to automatic create Convolutional Neural Network in PyTorch
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
PyTorch code for "Motor Imagery Decoding Using Ensemble Curriculum Learning and Collaborative Training"
Labs for 5003 Deep Learning Practice course in summer term 2021 at NYCU.
NCTU(NYCU) Deep Learning and Practice Spring 2021
EEG Classification API using Flask
Machine Learning based Brain Computer Interface (BCI) by analyzing EEG Data using PyTorch
EEGnet on a microcontroller
Stage training Implementation
Project for XAI606(Korea University)
Processing EEG data using Speechbrain-MOABB and model tuning to get best results
NYCU Deep Learning and Practice Summer 2023
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