<div dir="ltr">Dear Makoto,<div><br></div><div>In your experience, does the remaining effect of gradient artefact appear in a single component (after <span style="font-family:arial,sans-serif;font-size:13px">singular value decomposition approach and </span>running ICA ) similar to other types of artefacts like muscle and blink components? If yes, what are the characteristics of the gradient component (in time, topography and frequential domains)? </div><div><br></div><div>Best regards,</div><div>Mori</div><div><br></div></div><div class="gmail_extra"><br><div class="gmail_quote">On 30 June 2014 18:48, Makoto Miyakoshi <span dir="ltr"><<a href="mailto:mmiyakoshi@ucsd.edu" target="_blank">mmiyakoshi@ucsd.edu</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">Dear Morin,<div><br></div><div>Generally speaking...</div><div><span class=""><div><br></div><div>> Could it be any number and which criteria needs to be considered? <br></div><div><br></div></span><div>Isn't it better to choose the one so that the original sampling rate is an integral multiple of the one chosen?</div><span class="">
<div><br></div><div>> should I have to remove gradient artefacts before running ICA and then trying to find the remaining effect of gradient artefact in ICA components?<br></div><div><br></div></span><div>Yes definitely. It is because grandient artifact has very high amplitude. Actually I recommend you try it yourself to see what happens. For artifact subtraction I liked Liu's singular value decomposition approach (NeuroImage 2012) because it does not smear out artifacts.</div>
<div><br></div><div>Makoto</div><div><br></div><div><div class="gmail_extra"><div class="gmail_quote"><div><div class="h5">On Mon, Jun 30, 2014 at 8:04 AM, mori larin <span dir="ltr"><<a href="mailto:morilarin88@gmail.com" target="_blank">morilarin88@gmail.com</a>></span> wrote:<br>
</div></div><blockquote class="gmail_quote" style="margin:0px 0px 0px 0.8ex;border-left-width:1px;border-left-color:rgb(204,204,204);border-left-style:solid;padding-left:1ex"><div><div class="h5"><div dir="ltr">Dear list,<div><br></div><div>I am using EEG data and I have two questions:</div>
<div>1) I am not sure about sampling rate. The EEG data was recorded at 5000 Hz and I have to down sample it for further work. I used 256 Hz and I do not know is it correct or not. How should we select the re-sampling rate? Could it be any number and which criteria needs to be considered? </div>
<div><br></div><div>2) For the EEG data which is recorded simultaneously with fMRI data, in order to remove gradient and BCG artefacts automatically from the data using ICA , should I have to remove gradient artefacts before running ICA and then trying to find the remaining effect of gradient artefact in ICA components? (and what are the methods to remove it) or I have to run ICA on the contaminated data directly? The latter I think I have to expect more components associated to gradient artefacts because the amplitude of the gradient artefacts are larger than brain signals. </div>
<div><br></div><div>I really appreciate it if you could help me,</div><div><br></div><div>Regards,</div><div>Morin</div></div>
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-- <br><div dir="ltr">Makoto Miyakoshi<br>Swartz Center for Computational Neuroscience<br>Institute for Neural Computation, University of California San Diego<br></div>
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