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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.
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
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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:
- Download and unzip the
.zip
file. - You will get a file named
pyHFO.dmg
. - Navigate to the directory containing the
pyHFO.dmg
file. - 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
The overview of the PyHFO is shown below:
The manual is available here.
This project is licensed under the UCLA Academic License - see the LICENSE file for details.
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