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Dear all,<br><br>I want to do a beamforming analysis using the LCMV beamformer on MEG resting state activity.<br><br>After the artifcat rejection, my data is chopped into trials of<br>different lengths.<br><br>My question is related as to how to compute the covariance matrix.<br><br>If I use<br><br>cfg = [];<br>cfg.vartrllength = 2;<br><br>cov = ft_timelockanalysis(cfg,data);<br><br>a covariance matrix will be computed for each trial while padding all the segments that are smaller than the longest segment with zeros.<br>I believe that in the end the average covariance matrix across the trials is used. Am I correct?<br><br>Another alternative would be to chop all the trials into epochs of the same length, ie 2 seconds.<br>cfg = [];<br>cfg.trllength = 2;<br><br>data = ft_redefinetrial(cfg,data);<br><br>cfg = [];<br>
cfg.vartrllength = 0;<br>
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cov = ft_timelockanalysis(cfg,data);<br><br>Finally, one could also concatenate all the trials into one long epoch and run the same code.<br><br>I would be curious to know what the differences would be regarding the computation of the covariance matrix<br>and which would be the most appropriate way to do this.<br><br>Any suggestions, help or comments would be highly appreciated.<br><br>Best, Fred<br>-- <br>Frédéric Roux, PhD student<br>Department of Neurophysiology<br>Max Planck Institute for Brain Research<br>D-60529 Frankfurt am Main<br>Frederic.Roux@brain.mpg.de<br>+49(0)69630183225<br><br><br> </div></body>
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