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Hi Ketan,
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<div class="">If you’re interested in the low frequencies, and not necessarily in the time dimension (i.e. how the amplitude of band-limited components vary across time), you probably would want to use ft_freqanalysis with the cfg.method = ‘mtmfft’.</div>
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<div class="">Low frequencies require longer segments of data in order to be estimated in a meaningful way.</div>
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With wavelet convolution, I am also getting a lot of smoothing/stretching in the frequency domain at frequencies above ~3 Hz and Im not sure what the right parameters are to avoid this. <br class="">
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<div>This is an inherent property of wavelet convolution, and cannot be avoided.</div>
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I have also attached example figures from both implementations (MTM and Wavelet convolution) for your reference. <br class="">
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Here are my questions:<br class="">
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1) Is there any way to avoid the loss of the data at the edges with mtmconvol? Also, do the parameters that I have used seem to be okay for what I am trying to do?<br class="">
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<div>See above. I would use cfg.method = ‘mtmfft’ to get a decent estimate of the lower frequencies. A 0.5 second time window only starts making sense for frequencies at which the time window is filled with a few full cycles worth of oscillation to begin with
(I’d say 6 Hz (but possibly more) and up).</div>
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2) With wavelet convolution, is it just that my epochs are not long enough to get at these low frequencies? Or am I getting the parameters wrong? <span style="font-family: Calibri, Arial, Helvetica, sans-serif; font-size: 16px; background-color: rgb(255, 255, 255);" class="">Any
help would be greatly appreciated.</span><br class="">
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<div>Nothing wrong with the parameters as such: it’s an interaction between the length of your data epochs and the wavelet parameters.</div>
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3) How can I avoid the smearing that I am getting for the higher frequencies using wavelet convolution?<br class="">
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<div>You cannot.</div>
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and finally,<br class="">
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4) At the moment, I am only doing the baseline normalisation when plotting the output of <span style="font-family: Menlo, Monaco, Consolas, "Courier New", monospace; font-size: 13px; white-space: pre; background-color: rgb(255, 255, 255);" class="">ft_freqanalysis</span>.
Is there any way to perform this baseline normalisation using <span style="font-family: Menlo, Monaco, Consolas, "Courier New", monospace; font-size: 13px; white-space: pre; background-color: rgb(255, 255, 255);" class="">ft_freqanalysis</span> itself?<br class="">
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<div>No. You’d either use ft_freqbaseline (when you have a time frequency represenation, and want to use some basline defined for the TFR), or use a direct contrast.</div>
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<div>Best wishes,</div>
<div>Jan-Mathijs</div>
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Thank you in advance for your help!<br class="">
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Regards,<br class="">
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<font size="2" class=""><span lang="NL-BE" style="font-size: 10pt; font-family: Arial, sans-serif; color: gray;" class="">Ketan Jaltare, M.Sc. | PhD Student</span><br class="">
<span lang="NL-BE" style="font-size: 10pt; font-family: Arial, sans-serif; color: gray;" class=""></span></font><span lang="NL-BE" style="font-size: 10pt; font-family: Arial, sans-serif; color: gray;" class=""><font size="2" class="">Research Group Health Psychology</font></span><br style="font-size: 16px;" class="">
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<span id="cid:85AC33BB-3E98-44FD-90C5-425DB8365BFF"><Figure_mtm.jpg></span><span id="cid:F59D0742-91DC-4583-92DC-F6611A0D1B1B"><Figure_wavelet.jpg></span><span style="caret-color: rgb(0, 0, 0); font-family: Calibri, Arial, Helvetica, sans-serif; font-size: 16px; font-style: normal; font-variant-caps: normal; font-weight: normal; letter-spacing: normal; text-align: start; text-indent: 0px; text-transform: none; white-space: normal; word-spacing: 0px; -webkit-text-stroke-width: 0px; background-color: rgb(255, 255, 255); text-decoration: none; float: none; display: inline !important;" class="">_______________________________________________</span><br style="caret-color: rgb(0, 0, 0); font-family: Calibri, Arial, Helvetica, sans-serif; font-size: 16px; font-style: normal; font-variant-caps: normal; font-weight: normal; letter-spacing: normal; text-align: start; text-indent: 0px; text-transform: none; white-space: normal; word-spacing: 0px; -webkit-text-stroke-width: 0px; background-color: rgb(255, 255, 255); text-decoration: none;" class="">
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<a href="https://urldefense.com/v3/__https://doi.org/10.1371/journal.pcbi.1002202__;!!HJOPV4FYYWzcc1jazlU!7WSnJ7tJY3b3neKmtg8WhXNGzL0f5qlF6KcBLE_LILhjCTXc7MtX0THDNNNJGzUy3o-DBEagwUKFKPDjRmkWCXWoidHoDnRLKk32kQ$" style="font-family: Calibri, Arial, Helvetica, sans-serif; font-size: 16px; font-style: normal; font-variant-caps: normal; font-weight: normal; letter-spacing: normal; orphans: auto; text-align: start; text-indent: 0px; text-transform: none; white-space: normal; widows: auto; word-spacing: 0px; -webkit-text-size-adjust: auto; -webkit-text-stroke-width: 0px; background-color: rgb(255, 255, 255);" class="">https://urldefense.com/v3/__https://doi.org/10.1371/journal.pcbi.1002202__;!!HJOPV4FYYWzcc1jazlU!7WSnJ7tJY3b3neKmtg8WhXNGzL0f5qlF6KcBLE_LILhjCTXc7MtX0THDNNNJGzUy3o-DBEagwUKFKPDjRmkWCXWoidHoDnRLKk32kQ$</a><span style="caret-color: rgb(0, 0, 0); font-family: Calibri, Arial, Helvetica, sans-serif; font-size: 16px; font-style: normal; font-variant-caps: normal; font-weight: normal; letter-spacing: normal; text-align: start; text-indent: 0px; text-transform: none; white-space: normal; word-spacing: 0px; -webkit-text-stroke-width: 0px; background-color: rgb(255, 255, 255); text-decoration: none; float: none; display: inline !important;" class=""></span><br style="caret-color: rgb(0, 0, 0); font-family: Calibri, Arial, Helvetica, sans-serif; font-size: 16px; font-style: normal; font-variant-caps: normal; font-weight: normal; letter-spacing: normal; text-align: start; text-indent: 0px; text-transform: none; white-space: normal; word-spacing: 0px; -webkit-text-stroke-width: 0px; background-color: rgb(255, 255, 255); text-decoration: none;" class="">
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