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<div>Dear All, </div>
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<div>I've been doing some testing with elekta neuromag data in Fieldtrip using different sensor types (meg, meggrad, megmag) and different preprocessing steps (tSSS 0.9 corr limit, no tSSS).</div>
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<div>A step that was proposed at the MEG UK 2015 demo was to use PCA to compensate for the ill-conditioned estimate of the cov/csd matrix due to maxfilter - could I ask why running a PCA and reducing the number of components further would compensate for this?
Apologies if this a naive question, however I would assume that you would not want to reduce the rank of your data further? <span style="font-size:10pt">Please see below for the link and code that I'm referring to. </span></div>
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<div><a href="http://www.fieldtriptoolbox.org/workshop/meg-uk-2015/fieldtrip-beamformer-demo" target="_blank"><font size="2">http://www.fieldtriptoolbox.org/workshop/meg-uk-2015/fieldtrip-beamformer-demo</font></a></div>
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<div><font size="2">%% deal with maxfilter</font></div>
<div><font size="2"> </font></div>
<div><font size="2">% the data has been maxfiltered and subsequently concatenated</font></div>
<div><font size="2">% this results in an ill-conditioned estimate of covariance or CSD</font></div>
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<div><font size="2">cfg = [];</font></div>
<div><font size="2">cfg.method = 'pca';</font></div>
<div><font size="2">cfg.updatesens = 'no';</font></div>
<div><font size="2">cfg.channel = 'MEGMAG';</font></div>
<div><font size="2">comp = ft_componentanalysis(cfg, data);</font></div>
<div><font size="2"> </font></div>
<div><font size="2">cfg = [];</font></div>
<div><font size="2">cfg.updatesens = 'no';</font></div>
<div><font size="2">cfg.component = comp.label(51:end);</font></div>
<div><font size="2">data_fix = ft_rejectcomponent(cfg, comp);</font></div>
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<div>Many thanks,</div>
<div>Mike Hall</div>
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