Hi Alex,<div><br></div><div>Thanks for your response. I am aware of the effect of discontinuities in data and was planning to make a note of where they occur and account for that in my analysis. The data is a 2 minutes long recording of spontaneous activity to be more precise. </div>
<div><br></div><div>Doing this through matlab works for me, but I just wanted to see if there was another way to do this type of analysis.</div><div><br></div><div>Thanks for your help!</div><div><br></div><div>Jonathan</div>
<div><br><div class="gmail_quote">On Wed, Jul 20, 2011 at 9:05 AM, Alexandre Gramfort <span dir="ltr"><<a href="mailto:gramfort@nmr.mgh.harvard.edu">gramfort@nmr.mgh.harvard.edu</a>></span> wrote:<br><blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex;">
Hi Avniel,<br>
<div class="im"><br>
> Actually, we did an analysis of how problematic taking out data this way would be in Ghuman, McDaniel, and Martin NeuroImage 2011 and it turns out that it depends on the analysis. If you end up doing a frequency domain analysis with wavelets, these edge effects get smoothed over pretty well, at least at higher frequencies (it depends on how large the chunks you cut out are, but with a generous window, this smoothing over is ok at frequencies > 2 Hz). I am guessing this is why Jonathan feels it is ok to do this (he is the McDaniel from that paper).<br>
<br>
</div>thanks for sharing your experience on this. To sum up, the answer is<br>
that it depends<br>
on what you do after with the data.<br>
<br>
to answer the initial question which was is there a build in way to do<br>
this, the answer is no.<br>
MNE unix commands cannot do this as far as I know and you need to use<br>
matlab or python<br>
to zero the artifact time segments.<br>
<br>
cheers,<br>
Alex<br>
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