[FieldTrip] denoising data

[Haiteng Jiang]

 Hi  Fred ,

   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 .

                                                           Best,
                                                        Haiteng


>
>
>
>
> Hi Ana, Haiteng and everyone else,
>
> I am joining in on this discussion as I am thinking about a way of
> combining
> ft_componentanalysis with ft_denoise_pca to remove EOG, ECG and EMG
> activity.
>
> The pipeline that I would like to try is the following:
>
> %% ICA decomposition
> cfg = [];
> cfg.method = 'runica';
>
> ICs = ft_componentanalysis(cfg,meg_data); % here meg_data has undergone
> basic preprocessing including bp-filtering as well as
>                                           & ft_artifact_jump and
> ft_artifact_clip
> %% Substract artifacts from ICA components
> cfg = [];
> cfg.refchannel = eeg_data.label(1); % eeg_data contains the EOG (1-2),
> ECG(3) and EMG(4) ref-channels
> cfg.channel = {'runica'};
> cfg.zscore = 'yes';
> cfg.pertrial = 'yes';
> cfg.trials = 'all';
>
> [ICs] = ft_denoise_pca(cfg,ICs,eeg_data); % substract VEOG activity from
> all ICs
> %%
>
> The idea is to repeat this step for all refchannels in order to substract
> the artifacts.
>
> Then backproject ICs to original data:
>
> %% Backproject data
> cfg = [];
> meg_data = ft_rejectcomponent(cfg,ICs,meg_data);
>
> The advantage is that you don't have to "throw away" the entire component
> as sometimes ICs
> contain a mix of artifacts and brain activity. So by regressing out the
> artifacts, you could
> avoid throwing away the good stuff.
>
> However, I don't know if this is a good approach. There may be pitfalls
> that I am not thinking
> of right now.
> Therefore any suggestions or comments about whether this is a good idea
> would be very welcome!
> I am hoping that some of the ft_guru's could drop a line or two about this!
>
> Fred
>
>
> ----- Original Message -----
> From: "Haiteng Jiang" <haiteng.jiang at gmail.com>
> To: fieldtrip at science.ru.nl
> Sent: Thursday, April 3, 2014 8:27:30 AM
> Subject: Re: [FieldTrip] denoising data
>
>
>
>
>
>
>
> 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
>
>
>
>


-- 
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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