System Identification Methods for Dynamic Models of Brain Activity

This repo is the implementation of our paper System Identification Methods for Dynamic Models of Brain Activity.

System Identification Methods for Dynamic Models of Brain Activity
Tristan Griffith, James Hubbard
Biomedical Signal Processing and Control
https://doi.org/10.1016/j.bspc.2021.102765

Reference

If you find this repo useful in your research, please cite our work. Gotta get those rookie numbers up.

@article{GRIFFITH2021102765,
title = {System identification methods for dynamic models of brain activity},
journal = {Biomedical Signal Processing and Control},
volume = {68},
pages = {102765},
year = {2021},
issn = {1746-8094},
doi = {https://doi.org/10.1016/j.bspc.2021.102765},
url = {https://www.sciencedirect.com/science/article/pii/S1746809421003621},
author = {Tristan D. Griffith and James E. Hubbard},
keywords = {System identification, Modal decomposition, EEG dynamics},}

Quick Start

1. Clone this repository

2. Run Classification Model of interest

Full Pipeline

1. Download Data

• DEAP data: The DEAP data set is publicly available, but requires access request here. It should be placed in the src/data folder
• EEGMMI data: Download the EEGMMI data set into the src/data folder of this repository using: wget -r -N -c -np https://physionet.org/files/eegmmidb/1.0.0/
• Alternatively, this repository contains the processed data by default. You can skip generating features and just run the subject classification model.

2. Create environment

• Open a terminal to the root of this repository, where environment.yml is located.

conda env create -f environment.yml

conda activate eegID

3. Generate Features

• Select Matlab script in src/features for data and algortithm of interest
  • src/features/DMDheatmaps.m generates the heatmaps and tabular input data for the DEAP dataset with the DMD algorithm
  • src/features/DMDheatmaps_Physio.m generates the heatmaps and tabular input data for the EEGMMI dataset with the DMD algorithm
  • src/features/OMAheatmaps.m generates the heatmaps and tabular input data for the DEAP dataset with the OMA algorithm
  • src/features/DMDheatmaps_Physio.m generates the heatmaps and tabular input data for the EEGMMI dataset with the OMA algorithm
• Modify heatmap and tabular output paths according to your needs

4. Run Classification Model

• Select the relevant Jupyter notebook for the generated features
• Modity the path variable to target your features instead of the preprocessed data
• Run the notebook

Project Organization

---

├── LICENSE
├── README.md          <- The top-level README for developers using this project.
├── data
│   ├── external       <- Data from third party sources.
│   ├── interim        <- Intermediate data that has been transformed.
│   ├── processed      <- The final, canonical data sets for modeling.
│   └── raw            <- The original, immutable data dump.
│
├── references         <- Data dictionaries, manuals, and all other explanatory materials.
│
├── reports            <- Generated analysis as HTML, PDF, LaTeX, etc.
│   └── figures        <- Generated graphics and figures to be used in reporting
│
├── requirements.txt   <- The requirements file for reproducing the analysis environment, e.g.
│                         generated with `pip freeze > requirements.txt`
│
├── src                <- Source code for use in this project.
│   ├── __init__.py    <- Makes src a Python module
│   │
│   ├── data           <- Scripts to download or generate data
│   │   └── make_dataset.py
│   │
│   ├── features       <- Scripts to turn raw data into features for modeling
│   │   └── build_features.py
│   │
│   ├── models         <- Scripts to train models and then use trained models to make
│   │   │                 predictions
│   │   ├── predict_model.py
│   │   └── train_model.py
│   │
│   └── visualization  <- Scripts to create exploratory and results oriented visualizations
│       └── visualize.py
│
└── tox.ini            <- tox file with settings for running tox; see tox.readthedocs.io

---

Project based on the cookiecutter data science project template. #cookiecutterdatascience