<html><body style="word-wrap: break-word; -webkit-nbsp-mode: space; -webkit-line-break: after-white-space; ">Dear Ondrej,<div><br></div><div>I have looked in detail in your detailed analysis.</div><div><br></div><div>First, thank you for looking in detail into that. Even though these messages are scary (we can never be sure that EEGLAB is bug free), we greatly appreciate that people like you take the time to test that the functions are doing what they are supposed to do.</div><div><br></div><div>Regarding your analysis, all of the problems you encounter are due to using the baseline option in a specific way on this artificial data (see below). First, you should try the "'baseline', NaN" option which does not perform any baseline. It is the first thing to do before trying other types of baseline. I am attaching here a screen copy of the decomposition on your data. We can clearly see the two frequencies that you have generated.</div><div><br></div><div><img height="345" width="732" apple-width="yes" apple-height="yes" src="cid:DA5882CB-6925-4EC7-A5AD-0BEB914911C5"></div><div>using your data, this is the command line call "figure; [ersp,itc,powbase,times,freqs,erspboot,itcboot] = newtimef(x, 8*2048, [-500 6000], 2048, 0,'baseline',NaN,'basenorm','off', 'maxfreq' ,20,'nfreqs',50,'padratio', 32, 'scale', 'abs');"</div><div><br></div><div>Then, comes the baseline. The graphs you produce are meaningful until you start using the 'basenorm' option. The "basenorm" option is used compute and show z scores (we prefer dB ourselves but some other researchers prefer to use z-score). In your case your baseline is from -500 ms to 500 ms. It means that the standard deviation will be 0 at most frequency except at 6 Hz. A standard deviation of 0 (when normalizing) makes the weights blow up to close to infinity (your power is 10^7 standard deviation). It does not totally blow up to infinity since the standard deviation of the baseline is not perfectly 0 but a very small number. The strange plots you are observing are due to that. I have looked into detail in the code of the function and I plotted all intermediary results from inside the function itself and there is no doubt about that.</div><div><br></div><div>Note also that the newtimef function was primarily designed to process data trials and not continuous data. This is the reason why the inter-trial coherence measure for your data returns meaningless results (ITC is only relevant for more than 1 trial).</div><div><br></div><div>Let me know if you have other questions or comments,</div><div><br></div><div>Arno</div><div><div><br><div><div>On May 7, 2010, at 8:53 AM, ondrej lassak wrote:</div><br class="Apple-interchange-newline"><blockquote type="cite"><div>I fed the TF analysis single sinusoid 6 and 12Hz and the TF plot shows multiple specral lines (more than two).<span class="Apple-converted-space"> </span><br>How can one rely on the TF when it introduces such massive artifacts both in pure FFT spectrogram and Wavelet scalogram?<br>Or am I doing something wrong? When only one freq during the whole time span is present the TF plots look like really bad moira and the presence of the freq is apparent only from the summation over time (left from the main plot).<br><br><br>The matlab report with function calls and resulting pictures is attached below (no scripts embedded in the html).<br><br><br><div id="avg_ls_inline_popup" style="position: absolute; z-index: 9999; padding-top: 0px; padding-right: 0px; padding-bottom: 0px; padding-left: 0px; margin-left: 0px; margin-top: 0px; width: 240px; overflow-x: hidden; overflow-y: hidden; word-wrap: break-word; color: black; font-size: 10px; text-align: left; line-height: 13px; visibility: hidden; display: inline; "><span><baseline eeglab TFs experiments.zip></span><span><ATT00001..txt></span></div></div></blockquote></div><br></div></div></body></html>