[FieldTrip] Maxfilter and PCA

Hall, Michael (Research Student) hallmbh at aston.ac.uk
Mon Sep 19 12:25:23 CEST 2016

Dear All,

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).

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? Please see below for the link and code that I'm referring to.


%% deal with maxfilter

% the data has been maxfiltered and subsequently concatenated
% this results in an ill-conditioned estimate of covariance or CSD

cfg = [];
cfg.method = 'pca';
cfg.updatesens = 'no';
cfg.channel = 'MEGMAG';
comp = ft_componentanalysis(cfg, data);

cfg = [];
cfg.updatesens = 'no';
cfg.component = comp.label(51:end);
data_fix = ft_rejectcomponent(cfg, comp);

Many thanks,
Mike Hall

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