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<p>Hello All,</p>
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<p> Apologies for reintroducing a question which has previously been covered: that of using ft_megrealign on data which is intended for use in MEG source localization. My understanding is that this algorithm changes the covariance structure between the channels
in such a way that localizations may be unstable afterwards (<a id="LPlnk420260" href="http://mailman.science.ru.nl/pipermail/fieldtrip/2012-May/005231.html">http://mailman.science.ru.nl/pipermail/fieldtrip/2012-May/005231.html</a>). Additionally, the handful
of published works using ft_megrealign appear to all be sensor-level analyses (5-6 unique results for "ft_megrealign" from google scholar).</p>
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<p> Nonetheless, I am trying to develop a group procedure for the tf_mixed_norm sparse localization algorithm in MNE-Python (Gramfort et al. 2013) , and it would be enormously beneficial to have the subjects "virtualized" onto a common head position (and
shape, but this problem might also be solved separately) so that their sensor-level measurement data could be combined into a grand average prior to localization.<br>
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<p> So my questions are, how detrimental might the ft_megrealign algorithm be expected to be to source localization, particularly a sparse localization such as the one I am using? In my application a minor loss of precision would be acceptable, but the localizations
need to remain generally correct. Does anyone know of an alternative way to achieve "virtualized" data in a common head position that might be more suitable? (I also need to avoid the assumption of temporal stationarity.)<br>
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<p> Thank you for your help,</p>
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<p>Per Lysne</p>
<p>University of New Mexico<br>
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