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Dear Jim,
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<div class="">Indeed, 3 samples is much too few for constructing the beamformer filter —as you guessed, this results in a poor covariance estimate that is furthermore low-rank and therefore difficult to reliably invert.</div>
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<div class="">But, it is no matter, as you can construct the filter with your complete trial (perhaps 100ms as a minimum length), but examine the resulting time series (source.avg.mom) for whichever shorter intervals you are interested in.</div>
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<div class="">If you are not contrasting conditions, you may consider applying:</div>
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<div class="">cfg.lcmv.weightnorm = 'nai’;</div>
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<div class="">For reference, our group typically uses the following additional options for such data:</div>
<div class="">cfg.lcmv.reducerank = 'no';</div>
<div class="">cfg.lcmv.fixedori = 'yes’;</div>
<div class="">cfg.lcmv.projectnoise = 'yes';</div>
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<div class="">It is so far undocumented, but our plotting tools in fieldtrip/contrib/nutmegtrip could help navigating the resulting source time series (in your path you’ll need: a full copy of SPM8, fieldtrip/contrib/nutmegtrip , and fieldtrip/template/atlas/aal
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<div class="">cfg=[];</div>
<div class="">cfg.mripath = ‘yourmri.nii’; % or template MRI if voxel grid is in MNI space</div>
<div class="">cfg.funparameter = 'avg.mom';</div>
<div class="">cfg.atlas = ft_read_atlas('ROI_MNI_V4.nii'); % AAL atlas (optional)</div>
<div class="">nmt_sourceplot_spm8(cfg,ft_convert_units(source,'mm'));</div>
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<div class="">Cheers,</div>
<div class="">Sarang</div>
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<div class="">On 01 Jun 2016, at 23:43, Jim Cavanagh <<a href="mailto:jim.f.cav@gmail.com" class="">jim.f.cav@gmail.com</a>> wrote:</div>
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<div dir="ltr" class="">I get radically different outputs depending on the time window used for LCMV (primarily MEG, but also a bit with EEG).
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<div class="">My data are sampled at 250 Hz, filtered .5 to 20 Hz, calculated as the percent change (sourcediff = (source A - source B) / source A). </div>
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<div class="">With 3 samples (12 ms), LCMV outcomes are uninterpretable (i.e. values of 100,000 % change). Increasing samples yield increasingly realistic values, especially at about 25 samples (100 ms). I presume this has to do with the covariance, especially
since there doesn't appear to be much noise in the data. This is unfortunate since some of the ERF events that I'd like to capture are short-lived.
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<div class="">1) Does anyone have an explanation for this issue so I can account for it?</div>
<div class="">2) Or, barring that, feedback on an optimal way to determine temporal window size for LCMV?<br class="">
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<div class="">-- <br class="">
<div class="gmail_signature" data-smartmail="gmail_signature">
<div dir="ltr" class="">Jim Cavanagh<br class="">
Assistant Professor<br class="">
University of New Mexico<br class="">
<a href="https://sites.google.com/site/jcavanaghunm/home" target="_blank" class="">https://sites.google.com/site/jcavanaghunm/home</a><br class="">
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