<HTML><BODY style="word-wrap: break-word; -khtml-nbsp-mode: space; -khtml-line-break: after-white-space; ">Hi Markus,<DIV><BR class="khtml-block-placeholder"></DIV><DIV><DIV><BLOCKQUOTE type="cite"><BLOCKQUOTE type="cite"><DIV style="margin-top: 0px; margin-right: 0px; margin-bottom: 0px; margin-left: 0px; ">Why do you remove these epochs? Do the signals saturate or is there excessive electrode(-cap) or subject movement in these?</DIV><DIV style="margin-top: 0px; margin-right: 0px; margin-bottom: 0px; margin-left: 0px; min-height: 14px; "><BR></DIV> </BLOCKQUOTE><DIV style="margin-top: 0px; margin-right: 0px; margin-bottom: 0px; margin-left: 0px; ">Yes, that's correct. I remove epochs before any further analysis, if I find saturated channels or excessive subject movements.</DIV></BLOCKQUOTE><DIV><BR class="khtml-block-placeholder"></DIV><DIV>Seems reasonable to me, then.</DIV><DIV><BR class="khtml-block-placeholder"></DIV><BR><BLOCKQUOTE type="cite"><BLOCKQUOTE type="cite"><DIV style="margin-top: 0px; margin-right: 0px; margin-bottom: 0px; margin-left: 0px; ">How many EEG channels do you have, by the way? I need to know in order to continue with the 'slightly longer answer'</DIV> </BLOCKQUOTE><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; ">I recorded my data from 60 Ag/Ag-Cl electrodes embedded in an elastic cap (with BrainAmp amplifiers) positioned according to the extended 10/20 system. Additionally, I record EOG with two electrodes placed at the outher canthi and one electrode placed below the left eye. All channels are referenced to the right mastoid during recording, while the left mastoid electrode is recorded as an additional active channel (off-line re-refrenced to the mean of both mastoids).</DIV></BLOCKQUOTE><DIV><BR class="khtml-block-placeholder"></DIV>Ok, thanks for this extra info. 60 channels is (I think) a good number in that you are less likely to be affected by the problem of over-fitting in ICA: this can sometimes happen when there are fewer source signals which are identifiable by the ICA algorithm (i.e., statistically independent signals with non-gaussian marginal distributions) than sensors. Note that fewer sources than sensors does not necessarily mean that your data is rank-deficient (you can easily check this by looking at the singular spectrum of your data matrix or, equivalently, at the spectrum of eigenvalues of the data covariance matrix - the former is gives the square root of the latter) because there is always some form of either "sensor" or "ambient" noise, which will have a gaussian distribution. The point is that when ICA has found all the obvious components (non-gaussian ones) it then tries to decompose the gaussian part of the signal into non-gaussian parts, and this will lead to so-called "over-fitting", which also affects the accuracy of the estimates of the "true" components. Different ICA algorithms behave differently in this case, some will give you spurious solutions, others will simply not converge to a solution (because there is none in the ICA sense, and throw an error of the code is smart enough to spot this). But you should not really have to worry too much about that; although do watch out for it: warnings and error messages about mixing or de-mixing matrix being (close to) singular is a good indication of over-fitting, and PCA-based dimension reduction of your data as part of the ICA is then recommended (though not a "magic" solution, either).</DIV><DIV><BR class="khtml-block-placeholder"></DIV><DIV>When there are fewer channels than sources, you can have the opposite problem, namely under-fitting. In this situation, there are not enough components to describe all of the data, and the solution is then likely to be some sort of compromise, where "true" sources that are "weak" are spread over several ICs, none of which individually describes a source. In this case the accuracy of your mixing matrix estimate will also be "off", but the estimates of the stronger components are usually pretty robust in this case.</DIV><DIV><BR class="khtml-block-placeholder"></DIV><DIV>What can also affect the accuracy of the mixing matrix estimate is the violation of the assumption of statistical independence of the sources, and this is pretty likely in the case of networks of neuronal populations displaying oscillatory activity with greater or lesser coherence because coherent signals (i.e. with a constant phase relationship other than 90 degrees) are generally not statistically independent. This need not be a disaster provided that the total activity of these coherent networks (i.e. in the signal subspace of coherent sources) is statistically independent of all the artifact stuff you wish to remove. Then you can go ahead with ICA and remove artifacts, but you will not necessarily be able to identify/interpret individual oscillatory components of brain activity. But since you do your analysis at the sensor level, this does not matter.</DIV><DIV><BR class="khtml-block-placeholder"></DIV><DIV>Another problem related to ICA and sources of oscillatory activity is that the amplitude time course of this activity is generally modulated over time, which in turn can make these sources have marginal distributions which "look" essentially Gaussian, in which case the ICA algorithm may again be unable to correctly (or uniquely) identify the sensor projections associated with such source signals, EVEN if they are statistically independent, since ICA ONLY works exactly if the sources are statistically independent AND non-gaussian.</DIV><DIV><BR class="khtml-block-placeholder"></DIV><DIV>So in summary, I guess my advice to you would be to always use EXTREME CAUTION when working with ICA :-), but to go ahead with what you're doing and just make sure you are able to robustly identity all of your artifacts, and don't spend too much time worrying about which components reflect brain activity, as this is likely to live in a subspace so individial topographies give a limited story. I really don't mean to sound negative, but these problems and limitations regularly occur in real life ICA-usage, and you need to know that it is not a "cure" for "bad" data (from an ICA point of view).</DIV><DIV><BR class="khtml-block-placeholder"></DIV><DIV>Hope this helps,</DIV><DIV>Christian</DIV><DIV><BR class="khtml-block-placeholder"></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. 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