That's clear now. Thank you so much!<div><br></div><div>Rob <span></span><br><br>On Thursday, February 11, 2016, Tzvetan Popov <<a href="mailto:tzvetan.popov@uni-konstanz.de">tzvetan.popov@uni-konstanz.de</a>> wrote:<br><blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex"><div style="word-wrap:break-word"><br><div>
<div><div style="word-wrap:break-word"><div>Hi Rob,</div><div>your observation is correct. In the particular method applied, i.e. spatial filtering, one considers always one node at the time. On the basis of the data covariance and the lead field the goal is to compute a set of weights that essentially allow for reconstructing the time course of activity at a given location independent from all others. The entire discretized brain volume is “scanned” eventually resulting in distributed source map. The observation you made becomes relevant when you estimate the activity at all locations at once, e.g. minimum-norm distributed source modeling.</div></div></div></div><div><br></div><div>best </div><div>tzvetan</div><div><br><blockquote type="cite"><div dir="ltr"><span style="font-size:12.8px">Dear FieldTrip Forum,</span><div style="font-size:12.8px"><br></div><div style="font-size:12.8px">I have a naive question. When carrying out this source analysis (<a href="http://www.fieldtriptoolbox.org/tutorial/networkanalysis" target="_blank">http://www.fieldtriptoolbox.org/tutorial/networkanalysis</a>), I believe there are data from ~275 sensors translated to data occupying ~372 nodes (or voxels) in source space. If this is true (i.e., larger number of source points derived from smaller number of sensors), does this create a margin of error in the source data? And if so, is this error quantifiable? </div><div style="font-size:12.8px"><br></div><div style="font-size:12.8px">Any advice on understanding this would be greatly appreciated.</div><div style="font-size:12.8px"><br></div><div style="font-size:12.8px">Thank you so much,</div><div style="font-size:12.8px"><br></div><div style="font-size:12.8px">Rob Reinhart</div></div>
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