<HTML><BODY><br><br>Dear Fieldtripers,<br><br>First of all big thanks for a wonderful tool and support!<br><br>I have some questions about cluster based permutation test on TF data.<br>I perform a between-trials experiment and want to search differences say <br>on frequencies 10-30Hz and latencies 200-1200 ms from onset.<br>So I need to explore these ranges for possible differencies in powerspectrum.<br>When I use for example<br> <br>cfg.latency = [0.2 1.2];<br>cfg.frequency = [10 30];<br><br>I find no significant clusters, and lowest p_value is very high, say 0,8.<br>But when I try to look at smaller time/frequency areas, like<br><br>cfg.latency = [0.2 0.3];<br>cfg.frequency = [15 16];<br><br>There can be one or two significant clusters with p_values<0.01.<br>So my question is: does the algorithm look for the whole specified TF window like<br>0.2-1.2s 10-30Hz and tries to find significant difference based on the whole length<br>of interval? This could explain why there are no significant clusters on bigger windows.<br>Such clusters appear when we shorten the toi and foi - it's much more probable to find differences<br>at smaller scales. <br>So the next question is: what should I do to explore the whole number of frequencies and latencies?<br>Shall I run a loop with parameters like this:<br><br>Iteration 1<br>cfg.latency = [0.2 0.3];<br>cfg.frequency = [15 16];<br><br>Iteration 2<br>cfg.latency = [0.3 0.4];<br>cfg.frequency = [15 16];<br><br>... and so on?<br><br>Or maybe I do not understand something in basis of method? <br>BTW, I've read all tutorials and appropriate papers like<br>Nonparametric statistical testing of EEG, but could not find the answer.<br><br><br>Thanks in advance,<br><br>Alex<br><br><br><br></BODY></HTML>