[Mne_analysis] mne beamformer lcmv

[Britta Westner]

Dear Hao,

the time window is needed to estimate the data covariance matrix, one of 
the "ingredients" for calculating the LCMV beamformer spatial filter 
that will be applied to your sensor space data. Generally, the estimate 
of this covariance matrix is better with more data samples. Thus, 
spatial filters constructed on small snippets of your data will be less 
reliable than spatial filters constructed on a longer time window.
Furthermore, if you use several time windows, i.e., several filters, I 
suspect that this can potentially lead to discontinuities in your source 
time series (if you intend to glue the output of your beamformers together).
Usually, to construct your data covariance matrix, you would use a time 
window of interest, representing the activity you are interested in.

I hope this helps,
Cheers,
Britta



Am 05.07.2017 um 10:13 schrieb mne_analysis-request at nmr.mgh.harvard.edu:

> From: jehherson chow<jehherson at gmail.com>
> Date: Wed, Jul 5, 2017 at 10:13 AM
> Subject: [Mne_analysis]  mne beamformer lcmv
> To:"mne_analysis at nmr.mgh.harvard.edu"  <mne_analysis at nmr.mgh.harvard.edu>
>
>
> Dear MNE Experts,
>
> I am using mne lcmv to reconstruct the sources. I find in mne I can
> apply lcmv beamformer on evoked data with a common spatial filter of
> all experimental conditions and the entire time window (e.g. from 0ms
> to 1000ms). The results look nice, but the method seems to be
> skeptical. It seems that it’s better to use a moving window instead of
> the entire window, which means that a moving window is kind of
> preserving the temporal resolution, while the entire time window
> sacrifice the temporal resolution. But the problematic thing of the
> moving window method is that it requires more than one spatial filter
> and the difference between these filters might make a confounding
> output. Do you know which the right way to do beamforming?
>
> Best,
> Hao


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