See also nirs.modules.BaselinePCAFilter
nirs.modules.PCAFilter
This module runs a PCA filter. Similar to the nirs.modules.BaselinePCAFilter, an alternative method is to use the same data file to define the components to be removed.
In this case, the decomposition [Eq. (1a)] is applied from the spatial covariance of the task dataset instead of a separate baseline-only data file. Because the principal components come from the same data file containing the task-based signal, the assumption is that the global spatial features of the systemic noise comprise a stronger component of the spatial covariance compared to the task-based signal. Equations (1a) and (1b) are used with the substitution of the Ytask (fNIRS data from the task file) in place of the separate baseline (Ybaseline) data in Eq. (1a). A limitation of this approach, however, is when the evoked brain signal of interest has a spatial extent (covariance) on the same scale as the systemic physiology, which is often true when the fNIRS probe contains only a few measurement channels and covers only the area of brain activity.
Example Usage:
for i = 1:4
raw(i,1) = nirs.testing.simData([], @(t)nirs.testing.blockedStimDesign(t,15,25,1));
% task-based 15s duration, 25s between onset, and 1 condition
end
raw(1).demographics(‘subject’) = ‘S1’; raw(1).demographics(‘task’) = ‘Tapping’;
raw(2).demographics(‘subject’) = ‘S2’; raw(2).demographics(‘task’) = ‘Tapping’;
raw(3).demographics(‘subject’) = ‘S1’; raw(3).demographics(‘task’) = ‘rest’;
raw(4).demographics(‘subject’) = ‘S2’; raw(4).demographics(‘task’) = ‘rest’;
nirs.createDemographicsTable(raw)
job = nirs.modules.KeepStims();
raw(3:4) = job.run(raw(3:4)); %Raw(3:4) are rest (do not have stim-mark)
%% Basic pipelines
job = nirs.modules.OpticalDensity;
job = nirs.modules.BeerLambertLaw(job);
job = nirs.modules.Resample(job);
hb = job.run(raw);
% PCA Filter
job = nirs.modules.PCAFilter();
job.ncomp=.8;
hb_baselinePCA2 = job.run(hb(find(ismember(nirs.createDemographicsTable(hb).task,‘Tapping’))));
References
[1] Zhang Y, Brooks DH, Franceschini MA, Boas DA. Eigenvector-based spatial filtering for reduction of physiological interference in diffuse optical imaging. J Biomed Opt. 2005 Jan-Feb;10(1):11014. doi: 10.1117/1.1852552. PMID: 15847580. https://pubmed.ncbi.nlm.nih.gov/15847580/
[2] Franceschini MA, Joseph DK, Huppert TJ, Diamond SG, Boas DA. Diffuse optical imaging of the whole head. J Biomed Opt. 2006 Sep-Oct;11(5):054007. doi: 10.1117/1.2363365. PMID: 17092156; PMCID: PMC2637816. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2637816/