Joint Decorrelation for Phase-locked Artifact Attenuation

This repository provides a reference implementations of the joint decorrelation (JD) ¹ method to attenuate phase-locked artifacts in neuroimaging data, as utilized in the accompanying research paper ².

File List

The following files are provided in this repository:

• demo.ipynb: Jupyter notebook that demonstrates the Python reference implementation to use joint decorrelation to attenuate eye blink and ECG artifacts in EEG data.

• jointdecorrelation.py: Python module that implements the joint decorrelation algorithm.

• jointdecorrelation.m: Matlab function that implements the joint decorrelation algorithm.

Usage

Local Installation

If you want to run it locally on your machine, Python3 and Jupyter are needed. The present code was developed and tested with the following packages:

- Python >= 3.8
- numpy
- scipy
- jupyter
- pyriemann

Make sure you have Python3 installed on your computer. You can then install the required packages (including Jupyter) by running

pip install -r requirements.txt

Finally, you can start a Jupyter session with

jupyter notebook

and open and run the demo.ipynb notebook.

Acknowledgements

This work was supported by Innovative Science and Technology Initiative for Security Grant Number JPJ004596, ATLA, Japan

This README file is based on the template by @klb2.

License and Referencing

This program is licensed under the MIT license. If you in any way use this code for research that results in publications, please cite the original article introducing joint decorrelation and our original article listed above.

You can use the following BibTeX entry

@article{de_cheveigne_joint_2014,
	title = {Joint decorrelation, a versatile tool for multichannel data analysis},
	volume = {98},
	doi = {10.1016/j.neuroimage.2014.05.068},
	journal = {NeuroImage},
	author = {de Cheveigné, Alain and Parra, Lucas C.},
	year = {2014},
	pages = {487--505}
}

@article{kuroda_test-retest_2024,
	title = {Test-retest reliability of {EEG} microstate metrics for evaluating noise reductions in simultaneous {EEG}-{fMRI}},
	doi = {10.1162/imag_a_00272},
	journal = {Imaging Neuroscience},
	author = {Kuroda, Toshikazu and Kobler, Reinmar J. and Ogawa, Takeshi and Tsutsumi, Mizuki and Kishi, Tomohiko and Kawanabe, Motoaki},
	year = {2024}
}

Footnotes

1. A. de Cheveigné and L. C. Parra, Joint decorrelation, a versatile tool for multichannel data analysis, NeuroImage, vol. 98, pp. 487–505, 2014, doi: 10.1016/j.neuroimage.2014.05.068. ↩

2. T. Kuroda, R. J. Kobler, T. Ogawa, M. Tsutsumi, T. Kishi, and M. Kawanabe, Test-retest reliability of EEG microstate metrics for evaluating noise reductions in simultaneous EEG-fMRI, Imaging Neuroscience, 2024, doi: 10.1162/imag_a_00272. ↩