[Mne_analysis] mne beamformer lcmv

[Britta Westner]

Hi Hao,

the data covariance matrix is directly involved in the computation of 
the beamformer weights, and therefore has an impact on the output of the 
beamformer. So, you may want to make sure that your covariance matrix is 
representative of the signal you are interested in and also a good 
estimate (involving enough data samples / a long enough time window).
The beamformer weights are then applied to your original time series, 
thus, your source signal has the same temporal resolution as the 
original signal (note that the beamformer is a spatial filter that 
defines the contributions of your channels to a given source space 
position).

Cheers,
Britta



Am 06.07.2017 um 00:33 schrieb 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 
>> <mailto: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:
>>
>>> 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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