[FieldTrip] ICA on neuromag data - invalid assumption of full rank data & mixed sensors?

[Hamid Mohseni]

Hi Peter,

Regarding your first question: after  maxfilter, you need to do a pca,
simply because your rank of data has been reduced from 306 to about 64. You
can do this by:

cfg        = [];
cfg.method = 'runica';
n_comp = rank(squeeze(data.trial{1}) * squeeze(data.trial{1})');
cfg.runica.pca = n_comp;
cfg.runica.stop = 1e-7;
comp = ft_componentanalysis(cfg, data);

Here, n_comp is the number of component and it is equal to the rank of data
set. If you don't do this, the algorithm takes a long time and normally
does not converge. Note that the data should be continuous and it is
highly recommended to remove big and jump artefacts before ICA (for example
using ft_databrowser). I found ICA using infomax 'runica' quite robust with
high accuracy fro removing EOG and ECG.


Regarding your second question: Using only 'MAG' to investigate the
topograph of component is fine, and better than 'GRAD', but have a look at
the components time-series as well. If you remove that component it will be
removed from the whole data set.




On 9 March 2013 01:56, Peter Goodin <pgoodin at swin.edu.au> wrote:

>  Hi Fieldtrip list,
>
>  Firstly I'd like to thank those who have asked questions using ICA on
> neuromag 306 data and the answers given. It's been extremely informative
> and taught me a lot about the general principles of ICA (it's no longer
> completely black magic, only partially...).
>
> I have two and a bit questions about using ICA on neuromag data, the first
> on the full rank assumption. After maxfiltering, the output data is rank
> deficient due to removal of the b-out components. I see that fastica is
> able to detect this and makes adjustments to the amounts of components
> extracted accordingly, but is doing this justified? What actually happens
> to the data if ICA is applied to a rank deficient data set? Are there any
> papers that examine this?
>
>  Secondly, I've been including all the meg channels (mags + grads) when
> running ICA (due to my interpretation of the conversation in
> http://mailman.science.ru.nl/pipermail/fieldtrip/2012-April/005016.html)
> then examining the time series components for those related to artifact
> (specifically ECG and EOG), while using a magnetometer only layout to view
> component topographies as a backup. My question (well, really a
> confirmation) is, by removing the artifact components calculated using the
> mixed sensors, does this remove the associated components from all the
> sensor types? Viewing pre and post ICA data appears to confirm this, but
> expert opinion is always good. The bit of a question is - is it acceptable
> to use a magnetometer only layout to interpret topoplots of the components
> if the components are based on mixed sensors? I've been using it because
> the readily identifiable artifact components have a distribution I'd expect
> from ECG and EOG as does the associated time series but I'm wanting to make
> sure.
>
>  Also, if this is the wrong forum to ask these questions can someone
> suggest one that might be a better fit?
>
>  Thanks again,
>
>  Peter.
>
>   __________________________
> Peter Goodin,
>  BSc (Hons), Ph.D Candidate.
>
>  Brain and Psychological Sciences Research Centre (BPsych)
> Swinburne University,
> Hawthorn, Vic, 3122
>
>  Monash Alfred Psychiatry Research Centre (MAPrc)
> Level 1, Old Baker Building
> Commercial Road
> Melbourne, Vic, 3004
>
> _______________________________________________
> fieldtrip mailing list
> fieldtrip at donders.ru.nl
> http://mailman.science.ru.nl/mailman/listinfo/fieldtrip
>



-- 
Hamid R. Mohseni, PhD
Post-Doctoral Research Assistant
Institute of Biomedical Engineering
University of Oxford, OX3 7DQ, UK
Tel: +44 (0) 1865 2 83826
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