Thanks Steve/Tarik/Andrew,<br><br>Of course it's alpha! I knew it would be something stupid and elementary like that. My EEG education so far has been very narrow, basically I've just been learning everything I need to know to troubleshoot this study as issues arise.<br>
<br>I should have known given how incredibly mind numbingly boring my study is.<br><br>Thanks again.<br><br>-Matthew<br><br><br><br><div class="gmail_quote">On Mon, Feb 27, 2012 at 2:56 PM, Stephen Politzer-Ahles <span dir="ltr"><<a href="mailto:politzerahless@gmail.com">politzerahless@gmail.com</a>></span> wrote:<br>
<blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex">Hi Matthew,<br><br>To me this looks a bit like alpha waves (common when a subject is getting tired, shutting their eyes, about to doze off, etc.) but I can't really be sure. I can't read the channel labels in your images, but alpha waves tend to show up the most robustly on posterior channels, so that would be one way to tell if that's what they are. If it is alpha waves, there are different opinions out there about what to do with them. Some experimenters usually reject stretches with alpha waves, whereas others believe they can often be left in if they're not too big (I believe Steve Luck's (2005) book has more information about this in the chapter on artifact rejection; I don't have a copy handy right now so I don't remember exactly what it says). In our lab we never try to remove them by filtering, because they tend to be in more or less the same frequency range as the (language-related) cognitive activity we're interested in; I'm not sure what frequency ranges you're interested in so I'm not sure if filtering would be appropriate or not. In my experience, alpha waves of about this size have seemed to get more or less averaged out of the ERPs if there are enough trials, but others on the list may be able to give more feedback.<br>
<br>Good luck,<br>Steve Politzer-Ahles<br><br><div class="gmail_quote"><div class="im">On Mon, Feb 27, 2012 at 7:56 AM, Matthew Stief <span dir="ltr"><<a href="mailto:ms2272@cornell.edu" target="_blank">ms2272@cornell.edu</a>></span> wrote:<br>
</div><blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex"><div><div class="h5">
Hi Everyone,<br><br>I was wondering if anyone has had any experience with the "<span>wavy</span>" activity that seems to go across almost all of the <span>channels</span>
in this subjects data. I am sure it is familiar to you but I have not seen it in any of the text books or guides I can find online and don't have an experienced person supervising me.<br><br>Here's what the strange activity looks like in filtered, cleaned, epoched, and baselined data...<br>
<br><a href="http://s1153.photobucket.com/albums/p512/mstief/?action=view¤t=wavychannels7.jpg" target="_blank">http://s1153.photobucket.com/albums/p512/mstief/?action=view¤t=wavychannels7.jpg</a><br>
<br><br>Going back to the same dataset before any processing has been
done, you can see the same waves. Obviously this is not particularly
clean data, but is it salvageable?<br><br><a href="http://s1153.photobucket.com/albums/p512/mstief/?action=view¤t=wavychannels8.jpg" target="_blank">http://s1153.photobucket.com/albums/p512/mstief/?action=view¤t=wavychannels8.jpg</a><br>
<br><br>Here's the exact same stretch of data after it has gone through 1hz high pass and 55hz low pass filters.<br><br><a href="http://s1153.photobucket.com/albums/p512/mstief/?action=view¤t=wavychannels10.jpg" target="_blank">http://s1153.photobucket.com/albums/p512/mstief/?action=view¤t=wavychannels10.jpg</a><br>
<br><br>It does seem to be affecting the ERPs at least to my untutored eye, here's one channel.<br><br><a href="http://s1153.photobucket.com/albums/p512/mstief/?action=view¤t=wavychannels4.jpg" target="_blank">http://s1153.photobucket.com/albums/p512/mstief/?action=view¤t=wavychannels4.jpg</a><br>
<br><br>So what is it? Normal? Horrible? Fatal or merely deleterious? Given that it seems to contaminate most or
all of the data for this person should I just throw it out or can I
get something out of it? In particular how will ICA handle it? Do I still have a chance of getting a stable decomposition out of it with a reliably identifiable P1? This person was particularly bad but there
are less drastic examples in several of my other participants. Keep in mind some of my participants are rather rare and would be difficult to replace (I study sexual orientation and some varieties are less common than others).<span><font color="#888888"><br clear="all">
<br>-- <br>_________________________________________________________________<br>Matthew Stief<br>Human Development | Sex & Gender Lab | Cornell University<br><a href="http://www.human.cornell.edu/HD/sexgender" target="_blank">http://www.human.cornell.edu/HD/sexgender</a><br>
<br><br>Heterosexuality isn't normal, it's just common.<br>-Dorothy Parker<br>
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<br clear="all"><br>-- <br>Stephen Politzer-Ahles<br>
University of Kansas<br>Linguistics Department<br><a href="http://www.linguistics.ku.edu/" target="_blank">http://www.linguistics.ku.edu/</a><br>
</font></span></blockquote></div><br><br clear="all"><br>-- <br>_________________________________________________________________<br>Matthew Stief<br>Human Development | Sex & Gender Lab | Cornell University<br><a href="http://www.human.cornell.edu/HD/sexgender" target="_blank">http://www.human.cornell.edu/HD/sexgender</a><br>
<br><br>Heterosexuality isn't normal, it's just common.<br>-Dorothy Parker<br>