📌EEGProgress

🍊 Overview: A fast and lightweight progressive convolution architecture for EEG processing and classification.

🍊 This project is based on our recent publication. You can access the original paper here: Link: EEGProgress.

🍊 In this study, a progressive convolution CNN architecture named "EEGProgress" is proposed, aiming to efficiently extract the topological spatial information of EEG signals from multi-scale levels (electrode, brain region, hemisphere, global) with superior speed.

🧰 How to run

🍊 You can directly run the code with “test.py”.

📕 Prerequisites

🍊 Before running the application, ensure that you have the following prerequisites installed:

  1. **Python:** The code is tested with Python 3.8. It should be compatible with most Python 3.x versions.
  
  2. **PyTorch:** This project requires PyTorch. If you haven't installed PyTorch yet, you can find installation instructions on the [official PyTorch website](https://pytorch.org/get-started/locally/).

📕 Settings

🍊 Once you have the environment set up, you can run "test.py" with customized settings:

  1. You can select the __"Raw/Permutated EEG Data"__ with the code:
  
     ```bash
     Current_Datasets = 'a19_SpPe/'  # Permutated data
     Current_Datasets = 'a20_SpRaw/'  # Raw data
     ```

  2. You can set the __"Number of Training Epoch"__ with the code:
      ```bash
      Epoch = 150 # The original setting of the epoch is 150
      ```

📌 Topological Permutation

🍊 The raw EEG data is permuted using the empirical topological permutation rule, integrating the EEG data with numerous topological properties.

<img src="ReadMe/TopologicalPermutation.png" alt="Topological Permutation" width="600" height="562">