# Conflict of preprocessing recommendations: ICA and high-pass
**URL:** https://mne.discourse.group/t/conflict-of-preprocessing-recommendations-ica-and-high-pass/1454
**Category:** Mailing List Archive (read-only)
**Tags:** list-archive
**Created:** [March 12, 2018, 9:59am UTC](https://mne.discourse.group/t/conflict-of-preprocessing-recommendations-ica-and-high-pass/1454 "2018-03-12T09:59:09Z")
**Posts on this page:** 3
**Page:** 1
### Author:  [@system](https://mne.discourse.group/u/system)
#### Post date: [March 12, 2018, 9:59am UTC](https://mne.discourse.group/t/conflict-of-preprocessing-recommendations-ica-and-high-pass/1454/1 "2018-03-12T09:59:09Z")
Dear MNE users,
I have a problem, that I would like to briefly state in three parts:
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### Author:  [@system](https://mne.discourse.group/u/system)
#### Post date: [March 12, 2018, 8:08pm UTC](https://mne.discourse.group/t/conflict-of-preprocessing-recommendations-ica-and-high-pass/1454/2 "2018-03-12T20:08:01Z")
hi Stefan,
I don't see any caveat with this approach. High pass filtering can allow
to better estimate the mixing matrix of ICA, then you can apply to broad
band
data if you like.
Best,
Alex
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### Author:  [@system](https://mne.discourse.group/u/system)
#### Post date: [March 13, 2018, 11:43am UTC](https://mne.discourse.group/t/conflict-of-preprocessing-recommendations-ica-and-high-pass/1454/3 "2018-03-13T11:43:35Z")
Hi Stefan!
What you are describing is in fact a standard approach to ICA. A good explanation why this is OK is given in Winkler et al. (2015): In theory, filtering does not change the ICA coefficients at all. Therefore, you can compute ICA on a 1Hz-filtered signals and then apply it to the same 0.1Hz-filtered signal. In practice, however, filtering does make a difference, because slowly changing drifts violate the stationarity assumption of ICA. Furthermore, filtering out nuisance signal parts tends to improve ICA decomposition because we are usually not interested in these low-frequency drifts. Taking these considerations together, computing and applying ICA on data filtered with different HP filters is a valid approach.
These references describe this approach in some detail:
I. Winkler, S. Debener, K.-R. M?ller, M. Tangermann. On the Influence of High-Pass Filtering on ICA-Based Artifact Reduction in EEG-ERP. In: Proceedings of the International Conference of the IEEE Engineering in Medicine and Biology Society, 2015:4101?4105, 2015. [https://doi.org/10.1109/EMBC.2015.7319296](https://doi.org/10.1109/EMBC.2015.7319296).
A. Hyv?rinen, J. Karhunen, E. Oja. Independent Component Analysis. New York: John Wiley & Sons, 2001.
S. Debener, J. Thorne, T. R. Schneider, F. C. Viola. Using ICA for the analysis of multi-channel EEG data. In: M. Ullsperger, S. Debener (Eds.), Simultaneous EEG and fMRI: Recording, Analysis, and Application, pp. 121-133. New York: Oxford University Press, 2010.
J. M. Pignat, O. Koval, D. V. D. Ville, S. Voloshynovskiy, C. Michel, T. Pun. The impact of denoising on independent component analysis of functional magnetic resonance imaging data. Journal of Neuroscience Methods, 213(1), 105-122, 2013.
There are lots of published studies that do not describe why this works, but which use it in their analyses, for example:
C. L. Baldwin, J. D. Lee, N. Lerner, J. S. Higgins. Detecting and quantifying mind wandering during simulated driving. Frontiers in Human Neuroscience, 11, 406, 2017.
E. Jungnickel, K. Gramann. Mobile Brain/Body Imaging (MoBI) of physical interaction with dynamically moving objects. Frontiers in Human Neuroscience, 10, 306, 2016.
S. Meyberg, W. Sommer, O. Dimigen. How microsaccades relate to lateralized ERP components of spatial attention. Neuropsychologia, 99, 64-88, 2017.
HTH,
Clemens