high pass filtering

Mailing List Archive (read-only)
1

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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2

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

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

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

3

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

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

4

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

and

http://mne-tools.github.io/mne-biomag-group-demo/auto_examples/plot_fanning.html#sphx-glr-auto-examples-plot-fanning-py

Mainak