<div dir="ltr"> Hi Fred , <div><br></div><div> This is an interesting/smart pipeline. However, I don't think it is proper to use EEG VEOG/ECG channel as reference on the IC component. The argument is that character of artifact components in EEG and MEG are different though similar . </div>
<div> </div><div> Best,</div><div> Haiteng </div><div><div class="gmail_extra">
<br><div class="gmail_quote"><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"><br>
<br>
<br><br>
<br>
Hi Ana, Haiteng and everyone else,<br>
<br>
I am joining in on this discussion as I am thinking about a way of combining<br>
ft_componentanalysis with ft_denoise_pca to remove EOG, ECG and EMG activity.<br>
<br>
The pipeline that I would like to try is the following:<br>
<br>
%% ICA decomposition<br>
cfg = [];<br>
cfg.method = 'runica';<br>
<br>
ICs = ft_componentanalysis(cfg,meg_data); % here meg_data has undergone basic preprocessing including bp-filtering as well as<br>
& ft_artifact_jump and ft_artifact_clip<br>
%% Substract artifacts from ICA components<br>
cfg = [];<br>
cfg.refchannel = eeg_data.label(1); % eeg_data contains the EOG (1-2), ECG(3) and EMG(4) ref-channels<br>
cfg.channel = {'runica'};<br>
cfg.zscore = 'yes';<br>
cfg.pertrial = 'yes';<br>
cfg.trials = 'all';<br>
<br>
[ICs] = ft_denoise_pca(cfg,ICs,eeg_data); % substract VEOG activity from all ICs<br>
%%<br>
<br>
The idea is to repeat this step for all refchannels in order to substract the artifacts.<br>
<br>
Then backproject ICs to original data:<br>
<br>
%% Backproject data<br>
cfg = [];<br>
meg_data = ft_rejectcomponent(cfg,ICs,meg_data);<br>
<br>
The advantage is that you don't have to "throw away" the entire component as sometimes ICs<br>
contain a mix of artifacts and brain activity. So by regressing out the artifacts, you could<br>
avoid throwing away the good stuff.<br>
<br>
However, I don't know if this is a good approach. There may be pitfalls that I am not thinking<br>
of right now.<br>
Therefore any suggestions or comments about whether this is a good idea would be very welcome!<br>
I am hoping that some of the ft_guru's could drop a line or two about this!<br>
<br>
Fred<br>
<br>
<br>
----- Original Message -----<br>
From: "Haiteng Jiang" <<a href="mailto:haiteng.jiang@gmail.com">haiteng.jiang@gmail.com</a>><br>
To: <a href="mailto:fieldtrip@science.ru.nl">fieldtrip@science.ru.nl</a><br>
Sent: Thursday, April 3, 2014 8:27:30 AM<br>
Subject: Re: [FieldTrip] denoising data<br>
<br>
<br>
<br>
<br>
<br>
<br>
<br>
Dear Ana,<br>
<br>
<br>
If I understand correctly , we use ft_denoise_synthetic and ft_denoise_pca to reduce noise due to recording (e.g. environment noise , thermal noise... ) not the brain signal artefact (e.g eye blink). Normally ,I use these two functions in the very early stage .<br>
<br>
Best,<br>
Haiteng<br>
<br>
<br>
<br>
Message: 5<br>
Date: Thu, 3 Apr 2014 00:54:42 +0200 (CEST)<br>
From: "Todorovic, A." < <a href="mailto:a.todorovic@fcdonders.ru.nl">a.todorovic@fcdonders.ru.nl</a> ><br>
To: FieldTrip List < <a href="mailto:fieldtrip@donders.ru.nl">fieldtrip@donders.ru.nl</a> ><br>
Subject: [FieldTrip] denoising data<br>
Message-ID:<br>
< <a href="mailto:124252612.215995.1396479282885.JavaMail.root@monoceros.zimbra.ru.nl">124252612.215995.1396479282885.JavaMail.root@monoceros.zimbra.ru.nl</a> ><br>
Content-Type: text/plain; charset=utf-8<br>
<br>
Dear 'trippers,<br>
<br>
I'm curious to hear your thoughts on how to best denoise data. What I'm doing is working, but I'd like to hear whether it's logical or reasonable.<br>
<br>
In particular, I have two issues. One is that I use ICA to remove blinks, so I have to fit denoising into a pipeline that incorporates ICA (which, I am guessing, works well only without including reference channels).<br>
<br>
The other is that I see there are two functions for denoising, ft_denoise_synthetic and ft_denoise_pca.<br>
<br>
In sum (1) I'm not really sure WHEN the best moment for denoising is, given that I use ICA, and (2) I don't understand when it's better to use ft_denoise_synthetic and when ft_denoise_pca. I do see that ft_denoise_pca has the option of preprocessing reference channels separately, which makes it easier to use after ICA.<br>
<br>
I used to know only about the first function, and my solution was to do ft_denoise_synthetic (using the 'G3BR' option) prior to the ICA. Then I would remove components which contain artifacts, and continue with making ERF/TFRs/whatever. This produces data that is somewhat cleaner than when I skip the denoising step.<br>
<br>
I was curious about the ft_denoise_pca when I saw it, so I tried running it on filtered, preprocessed data that I had after I rejected ICA components. [In the process of doing ICA, I used the ft_denoise_synthetic option, as above.] This produced a different TFR at the end of the road, which again looked cleaner. Significantly cleaner, actually, but N=1.<br>
<br>
Now I'm not sure if it was a logical step to use both denoising functions, and if it would have been a better idea to do things differently. I'd like to hear both whether something in the logic is wrong, and whether it's inelegant.<br>
<br>
Cheers,<br>
Ana<br>
<br>
<br><br>
</blockquote></div><br><br clear="all"><div><br></div>-- <br><div><div><font face="arial, helvetica, sans-serif">Haiteng Jiang</font></div><div><font face="arial, helvetica, sans-serif">PhD candidate</font></div><div><font face="arial, helvetica, sans-serif">Neuronal Oscillations Group</font></div>
<div><font face="arial, helvetica, sans-serif">Donders Institute for Brain, Cognition and Behaviour</font></div><div><font face="arial, helvetica, sans-serif">Centre for Cognitive Neuroimaging</font></div><div><font face="arial, helvetica, sans-serif">Radboud University Nijmegen</font></div>
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