/pyHFO

STE/MNI HFO detection, classification and visualization

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PyHFO

ProjectPage | Download | Manual |

PyHFO is a multi-window desktop application providing an integrated and user-friendly platform that includes time-efficient HFO detection algorithms such as short-term energy (STE) and Montreal Neurological Institute and Hospital (MNI) detectors and deep learning models for artifact and HFO with spike classification.

Bibtex

If you find our project is useful in your research, please cite:

Zhang, Y., Liu, L., Ding, Y., Chen, X., Monsoor, T., Daida, A., Oana, S., Hussain, S. A., Sankar, R., Fallah, A., Santana-Gomez, C., Engel, J., Staba, R. J., Speier, W., Zhang, J., Nariai, H., & Roychowdhury, V. (2024). PyHFO: lightweight deep learning-powered end-to-end high-frequency oscillations analysis application. Journal of neural engineering, 10.1088/1741-2552/ad4916. Advance online publication. https://doi.org/10.1088/1741-2552/ad4916

Related Projects

  • HFODetector - A Python toolbox for very fast HFO detection.

  • HFO-Classification - Many HFO classification projects powered by deep learning.

  • EEG-Viz - A Python toolbox for EEG visualization.

Installation

You can download the latest version of PyHFO from the releases page.

If you choose to use the macOS version of the standalone distributable application, please follow these additional steps:

  1. Download and unzip the .zip file.
  2. You will get a file named pyHFO.dmg.
  3. Navigate to the directory containing the pyHFO.dmg file.
  4. Open the terminal and run the following command to remove the quarantine attribute:
xattr -cr pyHFO.dmg

You can also install it from the source code:

git clone https://github.com/roychowdhuryresearch/pyHFO.git 
cd pyHFO
pip install -r requirements.txt
python main.py

Usage

The overview of the PyHFO is shown below: Alt text

The manual is available here.

License

This project is licensed under the UCLA Academic License - see the LICENSE file for details.

Acknowledgments

Contributors:

This project is under supervsion of Prof. Vwani Roychowdhury.

Department of Electrical and Computer Engineering, University of California, Los Angeles

Division of Pediatric Neurology, Department of Pediatrics, UCLA Mattel Children’s Hospital David Geffen School of Medicine