NYCU Deep Learning and Practice Summer 2023

Machine Learning based Brain Computer Interface (BCI) by analyzing EEG Data using PyTorch

EEGnet on a microcontroller

NCTU(NYCU) Deep Learning and Practice Spring 2021

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".

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.

NYU CS-GY 9223 E Neuroinformatics (Spring 2024) - Final Project

Clasificador de cognitive tasks para señales EEG basado en EEGNet utilizando el dataset de Aunon y Keirn (1989)

Stage training Implementation

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

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

Labs for 5003 Deep Learning Practice course in summer term 2021 at NYCU.

EEG Classification API using Flask

Project for XAI606(Korea University)

Class to automatic create Convolutional Neural Network in PyTorch

The codes that I implemented during my B.Sc. project.

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

EEG Artifact Removal Using Deep Learning (source code, IEEE Journal of Biomedical and Health Informatics)