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This project focuses on implementing CNN model based on the EEGNet architecture with Pytorch library for classifying motor imagery tasks using EEG data.
Created
2024-05-03
19 commits to main branch, last one 2 months ago
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 ...
Created
2024-08-13
13 commits to main branch, last one 3 months ago
Deep Learning pipeline for motor-imagery classification.
Created
2022-01-09
6 commits to master branch, last one 2 years ago
Improving performance of motor imagery classification using variational-autoencoder and synthetic EEG signals
Created
2021-10-14
11 commits to main branch, last one 3 years ago
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), ...
Created
2023-11-12
6 commits to main branch, last one 4 months ago
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...
Created
2024-02-06
12 commits to main branch, last one 9 months ago