[AdC]
[Equipe Audition]
[CNRS]
[ENS]
[IEC]
[LSP]
NoiseTools
NoiseTools is a Matlab toolbox to denoise and analyze multichannel
electrophysiological data, such as from EEG, MEG, electrode arrays, optical
imaging, or fMRI. NoiseTools implements several new algorithms that are effective to
suppress various sources of environmental, sensor, and physiological noise.
Download
(old versions).
Overview and
automatically generated HTML
documentation.
Documentation is minimal, but a (very) few example scripts
are available together with data
to run them.
WARNING: this code is under development and may radically change
without notice. Make an archival copy of any code you use. DO NOT
EXPECT YOUR CODE TO WORK WITH NEWER VERSIONS. An archive of old versions is
available on the website.
Please cite:
- de Cheveigné, A. (2022). Local Subspace Pruning (LSP) for Multichannel Data Denoising, BioRxiv, https://doi.org/10.1101/2022.02.27.482148.
- de Cheveigné, A., Slaney, M., Fuglsang, Søren A. and and Hjortkjaer, J. (2021). Auditory Stimulus-response Modeling with a Match-Mismatch Task. Journal of Neural Engineering, 18, 046040, doi:10.1088/1741-2552/abf771.
- de Cheveigné, A (2021). Shared Component Analysis. NeuroImage, 226, 117614, doi: 10.1016/j.neuroimage.2020.117614.
- de Cheveigné, A. (2019). ZapLine: a simple and effective method to remove power line artifacts. NeuroImage, 207 116356.
- de Cheveigné, A., Nelken, I. (2019) Filters: why, when and how (not) to use them, Neuron, 102, 280-293.
- de Cheveigné, A., Di Liberto G.M., Arzounian D., Wong, D.D.E., Hjortkjaer, J., Fuglsang, S., Parra, L. (2019) Multiway canonical correlation analysis of brain signals. NeuroImage, 186, 728-740.
- de Cheveigné, A., Wong, DDE., Di Liberto, GM, Hjortkjaer, J., Slaney M., Lalor, E. (2018) Decoding the auditory brain with canonical correlation analysis. NeuroImage 172, 206-216, https://doi.org/10.1016/j.neuroimage.2018.01.033.
- de Cheveigné, A., Arzounian, D. (2018) Robust detrending, rereferencing, outlier detection, and inpainting for multichannel data. NeuroImage, 10.1016/j.neuroimage.2018.01.035.
- de Cheveigné A (2016) Sparse Time Artifact Removal, Journal of Neuroscience Methods, 262, 14-20, doi:10.1016/j.jneumeth.2016.01.005.
- de Cheveigné A, Arzounian D (2015) Scanning for oscillations, Journal of Neural Engineering, 12, 066020, DOI: http://iopscience.iop.org/article/10.1088/1741-2560/12/6/066020/meta.
- de Cheveigné, A., Parra, L. (2014), Joint decorrelation: a flexible tool for multichannel data analysis, Neuroimage, DOI: 10.1016/j.neuroimage.2014.05.068.
- de Cheveigné, A., Edeline, J.M., Gaucher, Q. Gourévitch, B.
(2013). "Component analysis reveals sharp tuning of the local field potential
in the guinea pig auditory cortex." J. Neurophysiol. 109, 261-272.
- de Cheveigné, A. (2012). "Quadratic component analysis." Neuroimage 59: 3838-3844.
[PDF]
- de Cheveigné, A. (2010). "Time-shift denoising source separation."
Journal of Neuroscience Methods 189: 113-120.
[PDF]
- de Cheveigné, A. and Simon, J. Z. (2008). "Denoising based on spatial filtering." Journal of Neuroscience Methods 171: 331-339.
[PDF]
- de Cheveigné, A. and Simon, J. Z. (2008). "Sensor Noise Suppression." Journal of Neuroscience Methods 168: 195-202.
[PDF]
- de Cheveigné, A. and Simon, J. Z. (2007). "Denoising based on Time-Shift PCA." Journal of Neuroscience Methods 165: 297-305.
[PDF]
[AdC]
[Equipe Audition]
[CNRS]
[ENS]
[IEC]
[LSP]