# high pass filtering

**URL:** <https://mne.discourse.group/t/high-pass-filtering/1103>\
**Category:** Mailing List Archive (read-only)\
**Tags:** list-archive\
**Created:** [October 20, 2016, 4:27pm UTC](https://mne.discourse.group/t/high-pass-filtering/1103 "2016-10-20T16:27:27Z")\
**Posts on this page:** 4\
**Page:** 1

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**Author:** ![system](https://global.discourse-cdn.com/free1/uploads/mne/original/1X/85cc6bd2b69cb698a166dc6d880fb550510d0144.jpeg) [@system](https://mne.discourse.group/u/system)\
**Post date:** [October 20, 2016, 4:27pm UTC](https://mne.discourse.group/t/high-pass-filtering/1103/1 "2016-10-20T16:27:27Z")

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Hi all,  
a quick question.  
I'm wondering if you'd have suggestion for which high pass filter to use on  
the ERP data?  
0.1Hz is common in ERP studies (particularly language that I'm working on)  
But from what I remember from the signal processing text books, 1Hz is  
safer regarding the slow drift movement artifacts.  
I'm wondering if someone has explored this further and/or has  
recommendations?

Many thanks  
Rezvan  
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**Author:** ![system](https://global.discourse-cdn.com/free1/uploads/mne/original/1X/85cc6bd2b69cb698a166dc6d880fb550510d0144.jpeg) [@system](https://mne.discourse.group/u/system)\
**Post date:** [October 20, 2016, 4:40pm UTC](https://mne.discourse.group/t/high-pass-filtering/1103/2 "2016-10-20T16:40:16Z")

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There has been quite a bit of discussion about high-pasing lately. Have you  
looked at the filtering tutorial in MNE-Python?

[https://mne-tools.github.io/stable/auto\_tutorials/plot\_background\_filtering.html](https://mne-tools.github.io/stable/auto_tutorials/plot_background_filtering.html)

You might in particular be interested in the pitfalls related to  
high-passing:

[https://mne-tools.github.io/stable/auto\_tutorials/plot\_background\_filtering.html#some-pitfalls-of-filtering](https://mne-tools.github.io/stable/auto_tutorials/plot_background_filtering.html#some-pitfalls-of-filtering)

Recent publications suggest that the appropriate choice of high-pass  
involves choosing tradeoffs based on the expected signal and noise  
characteristics of your recording, so unfortunately I don't know of a  
single best answer.

Eric

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**Author:** ![system](https://global.discourse-cdn.com/free1/uploads/mne/original/1X/85cc6bd2b69cb698a166dc6d880fb550510d0144.jpeg) [@system](https://mne.discourse.group/u/system)\
**Post date:** [October 20, 2016, 5:02pm UTC](https://mne.discourse.group/t/high-pass-filtering/1103/3 "2016-10-20T17:02:22Z")

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This paper is also a good resource on filtering ERP data:

@article{TannerEtAl2015,  
&nbsp;&nbsp;title = {How Inappropriate High-Pass Filters Can Produce Artifactual  
Effects and Incorrect Conclusions in {ERP} Studies of Language and  
Cognition},  
&nbsp;&nbsp;volume = {52},  
&nbsp;&nbsp;shorttitle = {High-Pass Filtering and Artifactual {{ERP}} Effects},  
&nbsp;&nbsp;doi = {10.1111/psyp.12437},  
&nbsp;&nbsp;number = {8},  
&nbsp;&nbsp;urldate = {2015-10-27},  
&nbsp;&nbsp;journal = {Psychophysiology},  
&nbsp;&nbsp;author = {Tanner, Darren and Morgan-Short, Kara and Luck, Steven J.},  
&nbsp;&nbsp;month = aug,  
&nbsp;&nbsp;year = {2015},  
&nbsp;&nbsp;pages = {997--1009}  
}

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**Author:** ![system](https://global.discourse-cdn.com/free1/uploads/mne/original/1X/85cc6bd2b69cb698a166dc6d880fb550510d0144.jpeg) [@system](https://mne.discourse.group/u/system)\
**Post date:** [October 20, 2016, 9:24pm UTC](https://mne.discourse.group/t/high-pass-filtering/1103/4 "2016-10-20T21:24:02Z")

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Also, do not miss out our scripts from the Biomag demo which are also meant  
to be educational to some extent:

[http://mne-tools.github.io/mne-biomag-group-demo/auto\_examples/plot\_filter.html#sphx-glr-auto-examples-plot-filter-py](http://mne-tools.github.io/mne-biomag-group-demo/auto_examples/plot_filter.html#sphx-glr-auto-examples-plot-filter-py)

and

[http://mne-tools.github.io/mne-biomag-group-demo/auto\_examples/plot\_fanning.html#sphx-glr-auto-examples-plot-fanning-py](http://mne-tools.github.io/mne-biomag-group-demo/auto_examples/plot_fanning.html#sphx-glr-auto-examples-plot-fanning-py)

Mainak