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Hi Noa,
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<div class="">Adding to Sarang’s clarifying points: if you have a steady-state stimulation paradigm, zero-lag correlation of the bilateral auditory response is a problem. In that case you could use the method described in: <a href="https://doi.org/10.3389/fnins.2018.00711" class="">https://doi.org/10.3389/fnins.2018.00711</a>.
The paper contains links to code (and data).</div>
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<div class="">Best wishes,</div>
<div class="">Jan-Mathijs</div>
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<div class="">On 24 Sep 2020, at 10:27, Sarang S. Dalal <<a href="mailto:sarang@cfin.au.dk" class="">sarang@cfin.au.dk</a>> wrote:</div>
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<div class="">Hi Noa,</div>
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<div class="">Thanks for your interest in our method. Just to clarify, our original approach was designed for time domain beamforming, but it seems you'd indeed need the one that Jan-Mathijs suggests for DICS / frequency-domain beamforming. But if you're still
interested, our very original time domain implementation is still distributed with the Nutmeg package I used to be involved with:</div>
<div class=""><a href="https://github.com/UCSFBiomagneticImagingLab/nutmeg/blob/master/beamformers/nut_Region_Suppression.m" class="">https://github.com/UCSFBiomagneticImagingLab/nutmeg/blob/master/beamformers/nut_Region_Suppression.m</a></div>
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<div class="">However, in our experience, the spatial correlation issue is less likely to occur for high frequency responses in general, since only a few milliseconds of delay between the left and right hemispheres is enough to substantially reduce the spatial
correlation between them and allow the beamformer to successfully separate the sources.</div>
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<div class="">Furthermore, back in 2006, it was common to perform beamforming based on the sample covariance of the averaged evoked responses. As it turns out, this exacerbated the problem of bilateral correlated sources. Since then, most beamformer implementations
(including FieldTrip, Nutmeg, and MNE-Python) compute the sample covariance using unaveraged trials by default; the trial-to-trial correlation between left and right auditory cortex is usually not nearly as high as it is in the average, which allows the sources
to be successfully separated. Even with standard auditory evoked responses, I've found that the cancellation is much less likely to occur when the sample covariance is computed this way.</div>
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<div class="">Although I haven't tried DICS for auditory responses, we've had great results with localizing high gamma band auditory MEG responses using time domain beamforming together with the Hilbert transform for the frequencies of interest. High gamma
band activity seems to be generally more difficult to resolve with EEG; that might be due to head model quality or simply lower sensitivity for the particular auditory cortex sources. To maximize EEG head model quality, I'd recommend using digitized electrode
positions and BEM head models generated from segmented individual MRIs.</div>
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<div class="">Hope that helps, and I'd be happy to discuss further.</div>
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<div class="">Cheers</div>
<div class="">Sarang </div>
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<div class="">On Thu, 2020-09-24 at 07:14 +0000, Schoffelen, J.M. (Jan Mathijs) wrote:</div>
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<font size="4" class="">Yes, this is implemented in FieldTrip, as per the following publication: <a href="https://doi.org/10.1016/j.neuroimage.2008.01.045" class="">https://doi.org/10.1016/j.neuroimage.2008.01.045</a></font>
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<div class=""><font size="4" class="">The method to use is ‘pcc’ in ft_sourceanalysis. Alternatively, you can handcraft your dipole specific forward models, to include both the ‘leadfield’ for the dipole of interest + the ‘leadfield’ of the to-be-suppressed
region. This requires some creative post-processing of the results, but that’s straightforward.</font></div>
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<div class=""><font size="4" class="">Best wishes,</font></div>
<div class=""><font size="4" class="">Jan-Mathijs</font></div>
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<div class="">On 23 Sep 2020, at 11:26, נועה הכהן <<a href="mailto:noa1hc@gmail.com" class="">noa1hc@gmail.com</a>> wrote:</div>
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<div dir="ltr" class="">Hello all,</div>
<div dir="ltr" class="">I am working on high dense eeg data, with auditory stimuli. I tried to use dics beamformer in order to localize the gamma waves, but due to the spatial correlation in the signal, I didn't get optimal results (as I understand it..). I
read about method that deal with this issue- by using weights for the coherent sources (<span style="font-family: arial, helvetica, clean, sans-serif;" class="">Modified Beamformers for Coherent Source Region Suppression, </span><a href="https://www.ncbi.nlm.nih.gov/pubmed/?term=Dalal%20SS%5BAuthor%5D&cauthor=true&cauthor_uid=16830939" target="_blank" style="color:rgb(100,42,143);white-space:nowrap;font-family:arial,helvetica,clean,sans-serif;font-size:13.4792px" class="">Sarang
S. Dalal</a><font face="arial, helvetica, clean, sans-serif" class=""><span style="font-size:13.4792px" class=""> ). Are there any applications of this (or similar) method in fieldtrip? </span></font></div>
<div dir="ltr" class=""><font face="arial, helvetica, clean, sans-serif" class=""><span style="font-size:13.4792px" class="">I will be glad to hear about this issue,</span></font></div>
<div dir="ltr" class=""><font face="arial, helvetica, clean, sans-serif" class=""><span style="font-size:13.4792px" class="">thanks, Noa</span></font></div>
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