Top GitHub Repositories for EEG Classification Using Dynamic Graphs

Top EEG classification based on dynamic graphs that have Github pubic code

There are several notable projects and repositories on GitHub that focus on EEG classification using dynamic graphs. These projects often leverage graph neural networks (GNNs) and other advanced machine learning techniques to analyze and classify EEG data. Here are some top repositories that you might find useful:

1. BCI-GNN:

   • Description: This repository provides code for a graph neural network (GNN) approach to brain-computer interface (BCI) applications. It focuses on classifying EEG signals using dynamic graph structures.
   • GitHub: BCI-GNN

2. EEG-GNN:

   • Description: This repository contains code for EEG classification using graph neural networks. It includes preprocessing steps, model training, and evaluation scripts.
   • GitHub: EEG-GNN

3. ST-GCN (Spatio-Temporal Graph Convolutional Networks):

   • Description: Although originally designed for action recognition, ST-GCN can be adapted for EEG classification by treating EEG channels as nodes in a graph and modeling their temporal dynamics.
   • GitHub: ST-GCN

4. DGCNN (Dynamic Graph CNN):

   • Description: This repository provides code for dynamic graph convolutional neural networks, which can be adapted for EEG signal classification by constructing dynamic graphs from EEG data.
   • GitHub: DGCNN

5. EEGNet:

   • Description: While not specifically using dynamic graphs, EEGNet is a compact convolutional neural network designed for EEG-based brain-computer interfaces. It can be combined with graph-based methods for enhanced performance.
   • GitHub: EEGNet

6. BrainGNN:

   • Description: This repository focuses on using graph neural networks for brain signal analysis, including EEG. It provides a framework for constructing and training GNNs on brain data.
   • GitHub: BrainGNN

7. PyTorch Geometric:

   • Description: While not specific to EEG, PyTorch Geometric is a library for deep learning on irregularly structured data like graphs. It can be used to implement custom GNNs for EEG classification.
   • GitHub: PyTorch Geometric

When working with these repositories, you may need to adapt the code to fit your specific EEG dataset and classification task. Additionally, combining these methods with domain-specific preprocessing and feature extraction techniques can further improve classification performance.