<html><head><meta http-equiv="Content-Type" content="text/html charset=us-ascii"></head><body style="word-wrap: break-word; -webkit-nbsp-mode: space; -webkit-line-break: after-white-space;"><div>Hi Stephen,</div><div><blockquote type="cite"><div dir="ltr"><div>However, before I go any further I am somewhat on my toes because projecting the power of the active or baseline period seems strangely shifted to the anterior. See attachment - red brains.</div></div></blockquote>could it be something trivial such as different MNI brain used for template_sourcemodel and plotting? Strange indeed?!</div><div><blockquote type="cite"><div dir="ltr"><div>Your comments have been very helpful thinking about the common filter. I actually now wonder whether it is a good idea at all in my case. One other thing could try is to somehow calculate a common filter by concatenating the baseline period and the active component period before doing calculating the covariance.</div><div>Or, alternatively, I could calculate the common filter over the whole post-stimulus interval (of say 300ms instead of 30ms of my component of interest), similarly as Johanna has done here: <a href="http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2899153/#R28">http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2899153/#R28</a></div></div></blockquote>Yes I would go for this too. Estimate the covariance on a longer snippet of data will ensure reliable estimate to begin with. Once you have the source time-series you can still zoom/focus on the 30ms component.</div><div>Alternatively, if you expect one source, e.g. right sensorimotor/left finger tapping task, you could also compute the covariance on the mean ERF and not over the single trials as you do now. Although some other probs might arise there it is probably worth trying.</div><div><br></div><div>cheers</div><div>Tzvetan</div><div><br></div></body></html>