[FieldTrip] denoising data
Haiteng Jiang
haiteng.jiang at gmail.com
Thu Apr 3 08:27:30 CEST 2014
>
> Dear Ana,
>
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 .
Best,
Haiteng
>
> Message: 5
> Date: Thu, 3 Apr 2014 00:54:42 +0200 (CEST)
> From: "Todorovic, A." <a.todorovic at fcdonders.ru.nl>
> To: FieldTrip List <fieldtrip at donders.ru.nl>
> Subject: [FieldTrip] denoising data
> Message-ID:
> <
> 124252612.215995.1396479282885.JavaMail.root at monoceros.zimbra.ru.nl>
> Content-Type: text/plain; charset=utf-8
>
> Dear 'trippers,
>
> 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.
>
> 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).
>
> The other is that I see there are two functions for denoising,
> ft_denoise_synthetic and ft_denoise_pca.
>
> 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.
>
> 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.
>
> 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.
>
> 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.
>
> Cheers,
> Ana
>
>
> ------------------------------
>
> Message: 6
> Date: Thu, 3 Apr 2014 07:53:36 +0200
> From: Haiteng Jiang <haiteng.jiang at gmail.com>
> To: fieldtrip at science.ru.nl
> Subject: Re: [FieldTrip] Question on Cluster-based permutation tests
> on time-frequency data
> Message-ID:
> <CAHSK_TQ5v_dOD6iOaqiu=
> 2+knQ6iHyZqSfyu0r2xvLessob65w at mail.gmail.com>
> Content-Type: text/plain; charset="iso-8859-1"
>
> Dear Nithin,
>
> Cluster statistic works both for MEG and EEG data since the machinery
> is the same . You can organize the Non-FT in the Fieldtrip style.
> Please not that FT have a data structure (channel*frequency*time ), so you
> need to transpose your data matrix first. Besides , you also need to
> define the layout of your channels , then you know the neighbors of
> channels (see ft_prepare_neighbours) because clusters are formed on the
> basis of temporal, spatial and spectral adjacency. For more information,
> please have a look at the tutorial again
> http://fieldtrip.fcdonders.nl/tutorial/cluster_permutation_freq.
>
> All
> the best,
>
> Haiteng
>
>
>
>
> >
> > Message: 6
> > Date: Wed, 2 Apr 2014 10:23:01 -0400
> > From: "Nithin Krishna" <nkrishna at mprc.umaryland.edu>
> > To: "list, FieldTrip discussion" <fieldtrip at science.ru.nl>
> > Subject: [FieldTrip] Question on Cluster-based permutation tests on
> > time-frequency data
> > Message-ID: <20140402T102301Z_155300170001 at mprc.umaryland.edu>
> > Content-Type: text/plain; charset="utf-8"
> >
> > Dear All,
> > I read the tutorial section on Cluster-based permutation tests on
> > time-frequency data as well as the Maris and Oostenveld, Journal of
> > Neuroscience Methods, 2007 report. I am interested in setting up a ERP
> > experiment on EEG data in this regard I like the procedure and processing
> > section of the tutorial, however I would like to know if there is any
> > section for EEG, I understand that megplanar is specific for MEG.
> > Also is it possible to use 3D matrix (TFR) (time, Freq channels ) non
> > feild trip output to run the cluster based permutation tests.
> > Looking forward
> > Nithin
> >
> >
> >
> > --
> Haiteng Jiang
> PhD candidate
> Neuronal Oscillations Group
> Donders Institute for Brain, Cognition and Behaviour
> Centre for Cognitive Neuroimaging
> Radboud University Nijmegen
>
> Visiting address
> Room 2.32
> Donders Centre for Cognitive Neuroimaging
> Kapittelweg 29
> 6525 EN Nijmegen
> the Netherlands
>
> Tel.: +31 (0)243668291
> Web: https://sites.google.com/site/haitengjiang/
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>
--
Haiteng Jiang
PhD candidate
Neuronal Oscillations Group
Donders Institute for Brain, Cognition and Behaviour
Centre for Cognitive Neuroimaging
Radboud University Nijmegen
Visiting address
Room 2.32
Donders Centre for Cognitive Neuroimaging
Kapittelweg 29
6525 EN Nijmegen
the Netherlands
Tel.: +31 (0)243668291
Web: https://sites.google.com/site/haitengjiang/
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