<HTML><BODY style="word-wrap: break-word; -khtml-nbsp-mode: space; -khtml-line-break: after-white-space; ">Hi Niki,<DIV><BR class="khtml-block-placeholder"></DIV><DIV>I fully concur with Stefan's advice and add a brief (technical) explanation for why doing ICA on time-locked averaged data is unlikely to give you a good separation.</DIV><DIV><BR class="khtml-block-placeholder"></DIV><DIV>One of the most common application of ICA to EEG/MEG data in cognitive neuroscience seems to be to remove (what the users assume/judge to be) artifact (i.e. non-brain or non-task related brain) activity from the data, and then to proceed with conventional sensor level analysis on the "clean" data. ICA assumes that the underlying source signals (component waveforms) in the data are statistically independent and have non-gaussian marginal distributions. If either of these assumptions are violated, then the sources are not uniquely identifiable w.r.t. the ICA search criteria, meaning that ICA will tend to give "funny" decompositions.</DIV><DIV><BR class="khtml-block-placeholder"></DIV><DIV>Now consider that (time-locked) averaging essentially involves summing your data over trials at each time point, and that the distribution of the sum of several random variables with any distribution (including non-gaussian) approaches a gaussian distribution in the limit. From this it is clear that time-locked averaging tends to make all non-time locked activity in your data (i.e. the stuff you want to remove, and need ICA to identify) appear MORE gaussian and therefore LESS identifiable from an ICA perspective. As a consequence, ICA cannot model the artifact activity sufficiently well to be able to separate it from your brain activity of interest.</DIV><DIV><BR class="khtml-block-placeholder"></DIV><DIV>Thus, if the aim of using ICA is to remove non-time locked artifacts (i.e. asynchronous oscillatory activity or intermittent transients) from your data, then following the reasoning above it is advisable to decompose the raw data (making sure to include examples of the artifact you wish to remove). Then either remove the artifacts and project back to the sensor space to do your averaging, or just do the averaging on the components themselves.</DIV><DIV><BR class="khtml-block-placeholder"></DIV><DIV>Hope this helps,</DIV><DIV>Christian</DIV><DIV><BR class="khtml-block-placeholder"></DIV><DIV><BR class="khtml-block-placeholder"></DIV><DIV><BR class="khtml-block-placeholder"></DIV><DIV><DIV><DIV>On 9 Mar 2007, at 13:11, Stefan Debener wrote:</DIV><BR class="Apple-interchange-newline"><BLOCKQUOTE type="cite"><DIV style="margin-top: 0px; margin-right: 0px; margin-bottom: 0px; margin-left: 0px; ">Hi Niki,</DIV><DIV style="margin-top: 0px; margin-right: 0px; margin-bottom: 0px; margin-left: 0px; min-height: 14px; "><BR></DIV><DIV style="margin-top: 0px; margin-right: 0px; margin-bottom: 0px; margin-left: 0px; ">It does, in my opinion, not make much sense to average first and then run ICA, you unlikely obtain a robust decomposition this way and may not be able to remove the stimulator artefact. ICA needs quite a few training data to find a decent decomposition, and this is not provided by ERPs, even though some commercial software packages may allow you to apply ICA to ERPs/ERFs. Only if (!) all your data are recorded such that there was no substantial head movement (that is, the position between sensors and sources did not change), you may get a decent artefact IC (or several ICs) from combined data (there is a few more ifs involved, please see previous ICA discussions here and in the EEGLAB mailing list). YI'd suggest using the pca option and decomposse, say, about 50 components, to start with, and then try to optimize the result by playing with the pca option and by running ICAs for separate recording blocks.</DIV><DIV style="margin-top: 0px; margin-right: 0px; margin-bottom: 0px; margin-left: 0px; ">Best,</DIV><DIV style="margin-top: 0px; margin-right: 0px; margin-bottom: 0px; margin-left: 0px; ">Stefan</DIV><DIV style="margin-top: 0px; margin-right: 0px; margin-bottom: 0px; margin-left: 0px; min-height: 14px; "><BR></DIV><DIV style="margin-top: 0px; margin-right: 0px; margin-bottom: 0px; margin-left: 0px; ">Niki Ray wrote:</DIV> <BLOCKQUOTE type="cite"><DIV style="margin-top: 0px; margin-right: 0px; margin-bottom: 0px; margin-left: 0px; ">Hi,</DIV><DIV style="margin-top: 0px; margin-right: 0px; margin-bottom: 0px; margin-left: 0px; "><SPAN class="Apple-converted-space"> </SPAN>I'm very new to fieldtrip. I have three questions. I have collected MEG</DIV><DIV style="margin-top: 0px; margin-right: 0px; margin-bottom: 0px; margin-left: 0px; ">data from patients with a deep brain stimulator implanted. I'd like to do an</DIV><DIV style="margin-top: 0px; margin-right: 0px; margin-bottom: 0px; margin-left: 0px; ">ICA to reject the stimulator related artifacts. Sometimes the stimulator is</DIV><DIV style="margin-top: 0px; margin-right: 0px; margin-bottom: 0px; margin-left: 0px; ">on and sometimes off. So my first question is, do i combine all the data</DIV><DIV style="margin-top: 0px; margin-right: 0px; margin-bottom: 0px; margin-left: 0px; ">together and then perform the ICA, or do i do different ICA analyses for</DIV><DIV style="margin-top: 0px; margin-right: 0px; margin-bottom: 0px; margin-left: 0px; ">each condition(on and off stimulation), and hence reject different</DIV><DIV style="margin-top: 0px; margin-right: 0px; margin-bottom: 0px; margin-left: 0px; ">components for each condition? <SPAN class="Apple-converted-space"> </SPAN></DIV><DIV style="margin-top: 0px; margin-right: 0px; margin-bottom: 0px; margin-left: 0px; "><SPAN class="Apple-converted-space"> </SPAN>One possibility for deciding wich of the artifacts were related to the</DIV><DIV style="margin-top: 0px; margin-right: 0px; margin-bottom: 0px; margin-left: 0px; ">stimulator was to use the example matlab script you provide for rejecting</DIV><DIV style="margin-top: 0px; margin-right: 0px; margin-bottom: 0px; margin-left: 0px; ">ECG components. Some of the channels (about 13, i'm using a ctf 275 system)</DIV><DIV style="margin-top: 0px; margin-right: 0px; margin-bottom: 0px; margin-left: 0px; ">have a lot of very distinct artifact that is obviously from the stimulator,</DIV><DIV style="margin-top: 0px; margin-right: 0px; margin-bottom: 0px; margin-left: 0px; ">so i wanted to use one of these channels as the "ECG" channel. Is this</DIV><DIV style="margin-top: 0px; margin-right: 0px; margin-bottom: 0px; margin-left: 0px; ">sensible, or not??<SPAN class="Apple-converted-space"> </SPAN></DIV><DIV style="margin-top: 0px; margin-right: 0px; margin-bottom: 0px; margin-left: 0px; "><SPAN class="Apple-converted-space"> </SPAN>Finally, I've been getting conflicting advice about whether it is okay to</DIV><DIV style="margin-top: 0px; margin-right: 0px; margin-bottom: 0px; margin-left: 0px; ">first average data, and then perform an ICA. What do you suggest is the</DIV><DIV style="margin-top: 0px; margin-right: 0px; margin-bottom: 0px; margin-left: 0px; ">right thing to do?</DIV><DIV style="margin-top: 0px; margin-right: 0px; margin-bottom: 0px; margin-left: 0px; min-height: 14px; "><BR></DIV><DIV style="margin-top: 0px; margin-right: 0px; margin-bottom: 0px; margin-left: 0px; ">Many thanks!</DIV><DIV style="margin-top: 0px; margin-right: 0px; margin-bottom: 0px; margin-left: 0px; ">Niki</DIV><DIV style="margin-top: 0px; margin-right: 0px; margin-bottom: 0px; margin-left: 0px; min-height: 14px; "><BR></DIV><DIV style="margin-top: 0px; margin-right: 0px; margin-bottom: 0px; margin-left: 0px; min-height: 14px; "><BR></DIV><P style="margin: 0.0px 0.0px 0.0px 0.0px; min-height: 14.0px"><SPAN class="Apple-converted-space"> </SPAN><BR class="khtml-block-placeholder"></P> </BLOCKQUOTE><DIV style="margin-top: 0px; margin-right: 0px; margin-bottom: 0px; margin-left: 0px; min-height: 14px; "><BR></DIV> </BLOCKQUOTE></DIV><BR><DIV> <SPAN class="Apple-style-span" style="border-collapse: separate; border-spacing: 0px 0px; color: rgb(0, 0, 0); font-family: Helvetica; font-size: 12px; font-style: normal; font-variant: normal; font-weight: normal; letter-spacing: normal; line-height: normal; text-align: auto; -khtml-text-decorations-in-effect: none; text-indent: 0px; -apple-text-size-adjust: auto; text-transform: none; orphans: 2; white-space: normal; widows: 2; word-spacing: 0px; "><SPAN class="Apple-style-span" style="border-collapse: separate; border-spacing: 0px 0px; color: rgb(0, 0, 0); font-family: Helvetica; font-size: 12px; font-style: normal; font-variant: normal; font-weight: normal; letter-spacing: normal; line-height: normal; text-align: auto; -khtml-text-decorations-in-effect: none; text-indent: 0px; -apple-text-size-adjust: auto; text-transform: none; orphans: 2; white-space: normal; widows: 2; word-spacing: 0px; "><SPAN class="Apple-style-span" style="border-collapse: separate; border-spacing: 0px 0px; color: rgb(0, 0, 0); font-family: Helvetica; font-size: 12px; font-style: normal; font-variant: normal; font-weight: normal; letter-spacing: normal; line-height: normal; text-align: auto; -khtml-text-decorations-in-effect: none; text-indent: 0px; -apple-text-size-adjust: auto; text-transform: none; orphans: 2; white-space: normal; widows: 2; word-spacing: 0px; "><SPAN class="Apple-style-span" style="border-collapse: separate; border-spacing: 0px 0px; color: rgb(0, 0, 0); font-family: Helvetica; font-size: 12px; font-style: normal; font-variant: normal; font-weight: normal; letter-spacing: normal; line-height: normal; text-align: auto; -khtml-text-decorations-in-effect: none; text-indent: 0px; -apple-text-size-adjust: auto; text-transform: none; orphans: 2; white-space: normal; widows: 2; word-spacing: 0px; "><SPAN class="Apple-style-span" style="border-collapse: separate; border-spacing: 0px 0px; color: rgb(0, 0, 0); font-family: Helvetica; font-size: 12px; font-style: normal; font-variant: normal; font-weight: normal; letter-spacing: normal; line-height: normal; text-align: auto; -khtml-text-decorations-in-effect: none; text-indent: 0px; -apple-text-size-adjust: auto; text-transform: none; orphans: 2; white-space: normal; widows: 2; word-spacing: 0px; "><DIV>----------------------------------------------------------------------</DIV><DIV>Christian Hesse, PhD, MIEEE</DIV><DIV><BR class="khtml-block-placeholder"></DIV><DIV style="margin-top: 0px; margin-right: 0px; margin-bottom: 0px; margin-left: 0px; ">F.C. Donders Centre for Cognitive Neuroimaging </DIV><DIV style="margin-top: 0px; margin-right: 0px; margin-bottom: 0px; margin-left: 0px; ">P.O. Box 9101 </DIV><DIV style="margin-top: 0px; margin-right: 0px; margin-bottom: 0px; margin-left: 0px; ">NL-6500 HB Nijmegen </DIV><DIV style="margin-top: 0px; margin-right: 0px; margin-bottom: 0px; margin-left: 0px; ">The Netherlands</DIV><DIV><BR class="khtml-block-placeholder"></DIV><DIV>Tel.: +31 (0)24 36 68293</DIV><DIV>Fax: +31 (0)24 36 10989</DIV><DIV><BR class="khtml-block-placeholder"></DIV><DIV>Email: <A href="mailto:c.hesse@fcdonders.ru.nl">c.hesse@fcdonders.ru.nl</A></DIV><DIV>Web: <A href="http://www.fcdonders.ru.nl">www.fcdonders.ru.nl</A></DIV><DIV>----------------------------------------------------------------------</DIV><DIV><BR class="khtml-block-placeholder"></DIV><DIV><BR class="khtml-block-placeholder"></DIV><SPAN class="Apple-style-span" style="border-collapse: separate; border-spacing: 0px 0px; color: rgb(0, 0, 0); font-family: Helvetica; font-size: 12px; font-style: normal; font-variant: normal; font-weight: normal; letter-spacing: normal; line-height: normal; text-align: auto; -khtml-text-decorations-in-effect: none; text-indent: 0px; -apple-text-size-adjust: auto; text-transform: none; orphans: 2; white-space: normal; widows: 2; word-spacing: 0px; "></SPAN><BR class="Apple-interchange-newline"></SPAN></SPAN></SPAN></SPAN></SPAN> </DIV><BR></DIV></BODY></HTML>