[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.
ADHDeepNet is a model that integrates temporal and spatial characterization, attention modules, and explainability techniques, optimized for EEG data ADAD diagnosis. Neural Architecture Search (NAS), Hyper-parameter optimization, and data augmentation are also incorporated to enhance the model's performance and accuracy.
This project focuses on implementing CNN model based on the EEGNet architecture with Pytorch library for classifying motor imagery tasks using EEG data.
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
This project explores the impact of Multi-Scale CNNs on the classification of EEG signals in Brain-Computer Interface (BCI) systems. By comparing the performance of two models, EEGNet and MSTANN, the study demonstrates how richer temporal feature extractions can enhance CNN models in classifying EEG signals
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
Processing EEG data using Speechbrain-MOABB and model tuning to get best results
EEGnet on a microcontroller
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