[Eeglablist] ASR Implications and Segmenting Based on First Occurrence of Event Code

[Nelson, Cailee]

Hi all,

I am having a bit of an issue preprocessing my data and was hoping someone could give me insight.

To give some background, we collected data from several different experimental paradigms within one EEG recording session so our file size is huge. For a paper I am working on, I really am only interested in the data from the last experimental paradigm we ran. When I go to band-pass filter the entire continuous EEG recording it takes forever (>90 minutes for one participant). As a result, I've tried selecting just the event codes of interest (there are only 2 events with 30 instances each). This helps filtering go much quicker. However, when I go to run clean_rawdata for channel rejection and ASR rejection it rejects about 23 channels and almost all of the data (keeps ~30 seconds from 192 seconds). I would really like to better understand this process as I want to automatically reject artifacts but have some concerns about it removing that much data.

When I visually inspect the data after being filtered it's clear that there are noisy channels and sections of data (see screenshot 1), but I don't think enough to only keep 30 seconds worth of data. When I visually inspect the ASR rejected data (see screenshot 2) it is clear that a lot of the data was rejected and you can also see that more data was rejected from that first segment which has changed the time length following each component. Should I try using a more lenient value for ASR? Is it better to reject epochs or trials instead in this case? Is it possible that segmenting to only keep the two events is causing the removal of a majority of the data?

I have tried to extract and reject epochs using artifact detection in epoched data from ERPLAB. I couldn't use the EEGLAB options for extracting and automatically rejecting epochs because when I went to extract epochs it gives an error saying dataset is empty (assuming this is because of the way I selected events so it no longer thinks the file is continuous?). However, when I do this it highlights almost all of the epochs for rejection (see screenshot 3). Could it be possible that it looks so noisy because of the way I originally segmented by selecting events?

My main question (especially if the segmenting by event codes is adding to the noise) is: is there a way that I can select just the last portion of the entire EEG recording that keeps it continuous? For example, I would like to write a bit of code that keeps the continuous data after the first occurrence of my event codes of interest and gets rid of everything beforehand but I'm not sure of a function that could do that as I'm relatively new to MATLAB coding.

Any help is greatly appreciated! Thank you!

Cailee

Screenshots: https://urldefense.com/v3/__https://alabama.box.com/s/1ans8hgmlc26g72p3zgnhsxjos2vboqc__;!!Mih3wA!AwwneLEf-KRh50nIeQJZK5yZel1Xaik63ApJW2gtHskipo8-ivdCkG20u-0_D8chGicQJoVBy9Is54H79rqVUW2U_hpd$ 

Cailee M. Nelson, Ph.D.
Postdoctoral Fellow
she/her/hers

Department of Psychology
Brain Research Across Development (B-RAD) Lab
University of South Carolina

caileen at mailbox.sc.edu<mailto:caileen at mailbox.sc.edu>


[cid:image001.jpg at 01D9E19B.1747C420]