<div dir="ltr">This paper is also a good resource on filtering ERP data:<br><br>@article{TannerEtAl2015,<br> title = {How Inappropriate High-Pass Filters Can Produce Artifactual Effects and Incorrect Conclusions in {ERP} Studies of Language and Cognition},<br> volume = {52},<br> shorttitle = {High-Pass Filtering and Artifactual {{ERP}} Effects},<br> doi = {10.1111/psyp.12437},<br> number = {8},<br> urldate = {2015-10-27},<br> journal = {Psychophysiology},<br> author = {Tanner, Darren and Morgan-Short, Kara and Luck, Steven J.},<br> month = aug,<br> year = {2015},<br> pages = {997--1009}<br>}<br><br><br><br></div><div class="gmail_extra"><br><div class="gmail_quote">On Thu, Oct 20, 2016 at 9:40 AM, Eric Larson <span dir="ltr"><<a href="mailto:larson.eric.d@gmail.com" target="_blank">larson.eric.d@gmail.com</a>></span> wrote:<br><blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex"><div dir="ltr">There has been quite a bit of discussion about high-pasing lately. Have you looked at the filtering tutorial in MNE-Python?<div><br></div><div><a href="https://mne-tools.github.io/stable/auto_tutorials/plot_background_filtering.html" target="_blank">https://mne-tools.github.io/<wbr>stable/auto_tutorials/plot_<wbr>background_filtering.html</a><br></div><div><br></div><div>You might in particular be interested in the pitfalls related to high-passing:</div><div><br></div><div><a href="https://mne-tools.github.io/stable/auto_tutorials/plot_background_filtering.html#some-pitfalls-of-filtering" target="_blank">https://mne-tools.github.io/<wbr>stable/auto_tutorials/plot_<wbr>background_filtering.html#<wbr>some-pitfalls-of-filtering</a><br></div><div><br></div><div>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.</div><div><br></div><div>Eric</div><div><br></div></div><div class="gmail_extra"><br><div class="gmail_quote"><div><div class="h5">On Thu, Oct 20, 2016 at 12:27 PM, Rezvan Farahi <span dir="ltr"><<a href="mailto:rezvan.farahi@gmail.com" target="_blank">rezvan.farahi@gmail.com</a>></span> wrote:<br></div></div><blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex"><div><div class="h5"><div dir="ltr">Hi all, <div>a quick question. </div><div>I'm wondering if you'd have suggestion for which high pass filter to use on the ERP data?</div><div>0.1Hz is common in ERP studies (particularly language that I'm working on)</div><div>But from what I remember from the signal processing text books, 1Hz is safer regarding the slow drift movement artifacts. </div><div>I'm wondering if someone has explored this further and/or has recommendations?</div><div><br></div><div>Many thanks</div><span class="m_2422389646756933452HOEnZb"><font color="#888888"><div>Rezvan</div></font></span></div>
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