[Old version] PyTorch implementation of EEGNet: A Compact Convolutional Network for EEG-based Brain-Computer Interfaces - https://arxiv.org/pdf/1611.08024.pdf

This project focuses on implementing CNN model based on the EEGNet architecture with Pytorch library for classifying motor imagery tasks using EEG data.

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

Deep Learning pipeline for motor-imagery classification.

Improving performance of motor imagery classification using variational-autoencoder and synthetic EEG signals

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 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".

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.

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"

Class to automatic create Convolutional Neural Network in PyTorch

This repo contains the source code of the project "FPGA implementation of BCIs using QCNNs" submitted to the Xilinx Open Hardware Design Competition 2021.

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

Stage training Implementation

EEGnet on a microcontroller