[Mne_analysis] mne beamformer lcmv

[jehherson chow]

Dear Britta,

Thank you for your answer. I am no expert into the algorithms. To compute data covariance matrix, I guess the time window is one dimension that could be used as the data samples, but trial number could also play the role in the other dimension. By select a time window, a common spatial filter would be created that representing all the data within the time window. My question is: will this kind of spatial filter make the source output temporally smoothed or "affected"? (For it collapses the data sample in the time dimension) Especially when there are more than one sources in the time window of interest.

Best,
Hao

> On Jul 5, 2017, at 5:36 PM, Britta Westner <britta.wstnr at gmail.com> wrote:
> 
> 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 <mailto:mne_analysis-request at nmr.mgh.harvard.edu>:
> 
>> From: jehherson chow <jehherson at gmail.com> <mailto: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" <mailto:mne_analysis at nmr.mgh.harvard.edu> <mne_analysis at nmr.mgh.harvard.edu> <mailto: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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