Denoising tools for M/EEG processing in Python 3.8+.
Disclaimer: The project mostly consists of development code, although some modules and functions are already working. Bugs and performance problems are to be expected, so use at your own risk. More tests and improvements will be added in the future. Comments and suggestions are welcome.
Automatic documentation is available online.
This code can also be tested directly from your browser using Binder, by clicking on the binder badge above.
This package can be installed easily using pip:
pip install meegkitOr you can clone this repository and run the following commands inside the
python-meegkit directory:
pip install -r requirements.txt
pip install .Note : Use developer mode with the -e flag (pip install -e .) to be able to modify
the sources even after install.
Some ASR variants require additional dependencies such as pymanopt. To install meegkit
with these optional packages, use:
pip install -e '.[extra]'or:
pip install meegkit[extra]Other available options are [docs] (which installs dependencies required to build the
documentation), or [tests] (which install dependencies to run unit tests).
If you use this code, you should cite the relevant methods from the original articles.
This is mostly a translation of Matlab code from the NoiseTools toolbox by Alain de Cheveigné. It builds on an initial python implementation by Pedro Alcocer.
Only CCA, SNS, DSS, STAR, ZapLine and robust detrending have been properly tested so far. TSCPA may give inaccurate results due to insufficient testing (contributions welcome!)
[1] de Cheveigné, A. (2019). ZapLine: A simple and effective method to remove power line
artifacts. NeuroImage, 116356. https://doi.org/10.1016/j.neuroimage.2019.116356
[2] de Cheveigné, A. et al. (2019). Multiway canonical correlation analysis of brain
data. NeuroImage, 186, 728–740. https://doi.org/10.1016/j.neuroimage.2018.11.026
[3] de Cheveigné, A. et al. (2018). Decoding the auditory brain with canonical component
analysis. NeuroImage, 172, 206–216. https://doi.org/10.1016/j.neuroimage.2018.01.033
[4] de Cheveigné, A. (2016). Sparse time artifact removal. Journal of Neuroscience
Methods, 262, 14–20. https://doi.org/10.1016/j.jneumeth.2016.01.005
[5] de Cheveigné, A., & Parra, L. C. (2014). Joint decorrelation, a versatile tool for
multichannel data analysis. NeuroImage, 98, 487–505.
https://doi.org/10.1016/j.neuroimage.2014.05.068
[6] de Cheveigné, A. (2012). Quadratic component analysis. NeuroImage, 59(4), 3838–3844.
https://doi.org/10.1016/j.neuroimage.2011.10.084
[7] de Cheveigné, A. (2010). Time-shift denoising source separation. Journal of
Neuroscience Methods, 189(1), 113–120. https://doi.org/10.1016/j.jneumeth.2010.03.002
[8] de Cheveigné, A., & Simon, J. Z. (2008a). Denoising based on spatial filtering.
Journal of Neuroscience Methods, 171(2), 331–339.
https://doi.org/10.1016/j.jneumeth.2008.03.015
[9] de Cheveigné, A., & Simon, J. Z. (2008b). Sensor noise suppression. Journal of
Neuroscience Methods, 168(1), 195–202. https://doi.org/10.1016/j.jneumeth.2007.09.012
[10] de Cheveigné, A., & Simon, J. Z. (2007). Denoising based on time-shift PCA.
Journal of Neuroscience Methods, 165(2), 297–305.
https://doi.org/10.1016/j.jneumeth.2007.06.003The base code is inspired from the original EEGLAB inplementation [1], while the Riemannian variant [2] was adapted from the rASR toolbox by Sarah Blum.
[1] Mullen, T. R., Kothe, C. A. E., Chi, Y. M., Ojeda, A., Kerth, T., Makeig, S.,
et al. (2015). Real-time neuroimaging and cognitive monitoring using wearable dry
EEG. IEEE Trans. Bio-Med. Eng. 62, 2553–2567.
https://doi.org/10.1109/TBME.2015.2481482
[2] Blum, S., Jacobsen, N., Bleichner, M. G., & Debener, S. (2019). A Riemannian
modification of artifact subspace reconstruction for EEG artifact handling. Frontiers
in human neuroscience, 13, 141.The code is based on Matlab code from Mike X. Cohen [1]
[1] Cohen, M. X., & Gulbinaite, R. (2017). Rhythmic entrainment source separation:
Optimizing analyses of neural responses to rhythmic sensory stimulation. Neuroimage,
147, 43-56.This code is based on the Matlab implementation from Masaki Nakanishi, and was adapted to python by Giuseppe Ferraro
[1] M. Nakanishi, Y. Wang, X. Chen, Y.-T. Wang, X. Gao, and T.-P. Jung,
"Enhancing detection of SSVEPs for a high-speed brain speller using task-related
component analysis", IEEE Trans. Biomed. Eng, 65(1): 104-112, 2018.
[2] X. Chen, Y. Wang, S. Gao, T. -P. Jung and X. Gao, "Filter bank canonical correlation
analysis for implementing a high-speed SSVEP-based brain-computer interface",
J. Neural Eng., 12: 046008, 2015.
[3] X. Chen, Y. Wang, M. Nakanishi, X. Gao, T. -P. Jung, S. Gao, "High-speed spelling
with a noninvasive brain-computer interface", Proc. Int. Natl. Acad. Sci. U.S.A,
112(44): E6058-6067, 2015.[1] Breunig M, Kriegel HP, Ng RT, Sander J. 2000. LOF: identifying density-based
local outliers. SIGMOD Rec. 29, 2, 93-104. https://doi.org/10.1145/335191.335388
[2] Kumaravel VP, Buiatti M, Parise E, Farella E. 2022. Adaptable and Robust
EEG Bad Channel Detection Using Local Outlier Factor (LOF). Sensors (Basel).
2022 Sep 27;22(19):7314. https://doi.org/10.3390/s22197314.The oscillator code is based on the Matlab implementation from Michael Rosenblum, and its corresponding paper [1]. The Endpoint Corrected Hilbert Transform (ECHT) method was adapted from [2].
[1] Rosenblum, M., Pikovsky, A., Kühn, A.A. et al. Real-time estimation of phase
and amplitude with application to neural data. Sci Rep 11, 18037 (2021).
https://doi.org/10.1038/s41598-021-97560-5
[2] Schreglmann, S. R., Wang, D., Peach, R. L., Li, J., Zhang, X., Latorre, A.,
... & Grossman, N. (2021). Non-invasive suppression of essential tremor via
phase-locked disruption of its temporal coherence. Nature communications, 12(1), 363.