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Current Workflow

The current workflow for the AMPLAB Digital Twins Project involves several key steps, which are outlined below:

  1. Data Acquisition: Collect raw EEG data from the Healthy Brain Network (HBN) dataset (release 10). Ensure that all raw data files are placed in the raw/ directory.

  2. Data Preprocessing: Use the filter.ipynb and processed_filter.ipynb notebooks to preprocess the raw EEG data.

    Notebooks Used

    • filter.ipynb: Performs low-pass filtering and artifact removal using ASR. (Saves to processed/ directory)
    • processed_filter.ipynb: Performs channel selection and further filtering of processed data. (Saves to filtered_processed/ directory)

  3. Task Separation: Utilize the separate_tasks_resting_state.ipynb notebook to separate different tasks (e.g., eyes open vs. eyes closed) from the resting-state EEG data.

  4. GAN Training: Train GAN models on the resting-state EEG data using the resting_state_gans.ipynb notebook. The generated models will be saved in the gan_data/ directory.

Workflow Visualization

flowchart LR
    A[Raw EEG Data<br/>Files in raw/] --> B[Low-Pass Filter &<br/>ASR<br/>filter.ipynb]
    B --> C[Processed EEG Data<br/>processed/]
    C --> D[Channel Filtering<br/>32 channels<br/>processed_filter.ipynb]
    D --> E[Filtered EEG Data<br/>filtered_processed/]
    E --> F[GANs Model<br/>resting_state_gans.ipynb]
    F --> G[Synthetic Samples<br/>synthetic_data/]

    style A fill:#e1f5ff
    style C fill:#fff4e1
    style E fill:#e8f5e9
    style G fill:#f3e5f5

Above is our current workflow for processing EEG data and training GAN models (turn on light mode if you can't see it!). All of these steps are listed to be of the data-preprocessing step of our project.

Research being research, feel free to modify and adapt the workflow as needed for your experiments!