<div dir="ltr">Hi Bingshuo,<div><br></div><div>I'm not sure what your code for the ICA is, but it may be beneficial to have the topoplots lined up with the signal, as I find it easier to recognize EOG and ECG artifacts in the signal. </div>
<div><br></div><div>The following tutorials explain ICA: </div><div><a href="http://fieldtrip.fcdonders.nl/example/use_independent_component_analysis_ica_to_remove_ecg_artifacts">http://fieldtrip.fcdonders.nl/example/use_independent_component_analysis_ica_to_remove_ecg_artifacts</a></div>
<div><a href="http://fieldtrip.fcdonders.nl/example/use_independent_component_analysis_ica_to_remove_eog_artifacts">http://fieldtrip.fcdonders.nl/example/use_independent_component_analysis_ica_to_remove_eog_artifacts</a><br>
</div><div><br></div><div>In the ECG tutorial, they give a line of code:</div><div><br></div><div><pre class="" style="padding:0.5em;margin-top:0px;font-size:12px;border:1px dashed rgb(204,204,204);color:rgb(0,0,0);overflow:hidden;font-family:Consolas,'Andale Mono',Menlo,Monaco,monospace;background-color:rgb(247,249,250);white-space:pre-wrap;width:640px;text-align:justify">
cfg = [];
cfg.channel = {comp.label{2:5} comp.label{15:18}}; % components to be plotted
cfg.layout = 'CTF275.lay'; % specify the layout file that should be used for plotting
ft_databrowser(cfg, comp)</pre></div><div class="gmail_extra">Which outputs the topoplot and the time course of the signal together in a component browser. </div><div class="gmail_extra"><br></div><div class="gmail_extra">
As far as criteria for correction, our policy is to err on the conservative side; only rejecting EOG and ECG artifacts, even if we find other components that are clearly artifactual (such as are picking up line noise).</div>
<div class="gmail_extra"><br></div><div class="gmail_extra">Without seeing your script, I can't really think of anything offhand that you are doing wrong, but I'm also not very familiar with TMS so there may be someone else who could answer that part of the question better.</div>
<div class="gmail_extra"><br></div><div class="gmail_extra">Good luck,</div><div class="gmail_extra"><br></div><div class="gmail_extra">Max Cantor<br>Research Assistant</div><div class="gmail_extra">Computational Neurolinguistics Lab</div>
<div class="gmail_extra">University of Michigan</div><div class="gmail_extra"><br><div class="gmail_quote">On Tue, Nov 26, 2013 at 12:26 PM, Bingshuo Li <span dir="ltr"><<a href="mailto:bingshuo.li@student.uni-tuebingen.de" target="_blank">bingshuo.li@student.uni-tuebingen.de</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"><div><div><div><div><div><div><div><div><div><div><div><div><div><div><div><div><div><div><div><div>Dear FieldTrip Community, <br>
<br></div>I recently started to analyze some TMS-EEG datasets and I encountered some questions regarding to using ICA to remove eye movement/muscle artifacts in our EEG data. As I am quite new to the analysis of TMS-EEG, I would like to inquire the FT community for some hints or suggestions. Below are the details of my questions:<br>
<br></div>//Description of Data Processing//<br></div><div>- EEG with 64 channel, sampling frequency 2500 Hz, electrode impedance less than 5 kOhm<br></div>- Every epoch consists of 1s prior to and 1s after TMS (130-150 trials per subject)<br>
</div>- TMS contaminated data points were cut out symmetrically -18ms to +18ms relative to TMS onset. Cubic spline interpolation is used to fill in the cut. <br></div>- Bandpass 0.5 - 80 Hz, with BUT and filter order 3. <br>
- Discrete Fourier transform filter (cfg.dftfilter) to remove 50 Hz line noise<br></div>- Visual inspection and rejection of trials with obvious unstable signal or channels.<br><br></div> //ICA//<br></div>- ICA algorithm: runica<br>
</div>- Demeaned data for ICA training (baseline is defined as the entire epoch -1 to +1s)<br></div>- Unmixing matrix applied to non-demeaned data for component removal <br><br></div>/////QUESTIONS/////<br></div>Please see the image below for a typical result of ICA from a subject with TMS applied at M1 (32 epochs for ICA training):<br>
<a href="https://www.dropbox.com/s/chwo2jnwi72saba/ica1.png" target="_blank">https://www.dropbox.com/s/chwo2jnwi72saba/ica1.png</a><br><br></div>Q1: It seems obvious to me that component 1 and 2 are of eyeblink origin. However, what about component 5, 12, 20, 28? Topology-wise, they seem to have a very anterior origin, but data in the time domain does not seem to correlate with component 1 and 2 very well (judging visually..)<br>
<br></div>Q2: What can you say about components 7, 9, 13 and 18? Are these cranial muscle artifacts? <br><br></div>Q3: Also, for components 42 and 54, given their high focality, are these more or less a indication of bad/unstable electrodes?<br>
<br></div>- I guess maybe I am asking too many questions. I think my main problem here is that I do not know what can be a good procedure / rules in manually selecting ICA components for rejection? (I tried to look in the literature but I couldn't find any that can answer my questions). And sometime I have the feeling that my ICA results look like a mess and maybe there were something wrong with my pre-processing or even data collection? <br>
<br></div>Thank you guys in advance for any input! I look forward to hearing from you!<br><br></div>Regards, <br><br></div>Bingshuo Li<br></div>MSc. Student, Neuroprosthetic Group, CIN, Uni Tübingen<br><br></div>
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