NYCU Deep Learning and Practice Summer 2023
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Updated
Sep 24, 2023 - Python
NYCU Deep Learning and Practice Summer 2023
NYU CS-GY 9223 E Neuroinformatics (Spring 2024) - Final Project
EEGnet on a microcontroller
Stage training Implementation
Processing EEG data using Speechbrain-MOABB and model tuning to get best results
Project for XAI606(Korea University)
Machine Learning based Brain Computer Interface (BCI) by analyzing EEG Data using PyTorch
EEG Classification API using Flask
NCTU(NYCU) Deep Learning and Practice Spring 2021
Labs for 5003 Deep Learning Practice course in summer term 2021 at NYCU.
PyTorch code for "Motor Imagery Decoding Using Ensemble Curriculum Learning and Collaborative Training"
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
Class to automatic create Convolutional Neural Network in PyTorch
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".
The codes that I implemented during my B.Sc. project.
EEG Artifact Removal Using Deep Learning (source code, IEEE Journal of Biomedical and Health Informatics)
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
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