# Time-frequency analysis, questions **URL:** \ **Category:** Mailing List Archive (read-only)\ **Tags:** list-archive\ **Created:** [May 17, 2020, 3:49pm UTC](https://mne.discourse.group/t/time-frequency-analysis-questions/2190 "2020-05-17T15:49:02Z")\ **Posts on this page:** 4\ **Page:** 1
**Author:** ![system](https://global.discourse-cdn.com/free1/uploads/mne/original/1X/85cc6bd2b69cb698a166dc6d880fb550510d0144.jpeg) [@system](https://mne.discourse.group/u/system)\ **Post date:** [May 17, 2020, 3:49pm UTC](https://mne.discourse.group/t/time-frequency-analysis-questions/2190/1 "2020-05-17T15:49:02Z")
External Email - Use Caution Dear MNE users, I am new to this field so I am sorry if the answer to my question is obvious. I need to do a time-frequency analysis and I am following this tutorial [https://mne.tools/dev/auto\_examples/time\_frequency/plot\_source\_label\_time\_frequency.html#sphx-glr-auto-examples-time-frequency-plot-source-label-time-frequency-py](https://mne.tools/dev/auto_examples/time_frequency/plot_source_label_time_frequency.html#sphx-glr-auto-examples-time-frequency-plot-source-label-time-frequency-py) as I am interested in obtaining this sort of maps in certain labels. However, the maps I am getting are definetely not normalized compared to the examples (see my examples attached - lower than 10Hz). 1. What might be the reason for this?Is there something special I need to do when computing inverse solution compared to typical evoked response analysis? (I include only noise covariance there) 2. What is the recommend parameter here for baseline\_mode (percent, logration mean - is there any suggestion on what to use and when?) 3. In this tutorial and command source\_induced\_power, what is the method used? Is it Morlet / Multitaper or something else? I found no information on this in contrast to e.g. tfr\_morlet commands used elsewhere? Thank you in advance. Sincerely yours, Kirill Elin, PhD \<[https://mne.tools/dev/generated/mne.minimum\_norm.source\_induced\_power.html#mne.minimum\_norm.source\_induced\_power](https://mne.tools/dev/generated/mne.minimum_norm.source_induced_power.html#mne.minimum_norm.source_induced_power)\> -------------- next part -------------- An HTML attachment was scrubbed... URL: [http://mail.nmr.mgh.harvard.edu/pipermail/mne\_analysis/attachments/20200517/6ea0bbb7/attachment-0001.html](http://mail.nmr.mgh.harvard.edu/pipermail/mne_analysis/attachments/20200517/6ea0bbb7/attachment-0001.html) -------------- next part -------------- A non-text attachment was scrubbed... Name: baseline\_mode='percent'.jpg Type: image/jpeg Size: 96187 bytes Desc: not available Url : [http://mail.nmr.mgh.harvard.edu/pipermail/mne\_analysis/attachments/20200517/6ea0bbb7/attachment-0001.jpg](http://mail.nmr.mgh.harvard.edu/pipermail/mne_analysis/attachments/20200517/6ea0bbb7/attachment-0001.jpg) ---
**Author:** ![system](https://global.discourse-cdn.com/free1/uploads/mne/original/1X/85cc6bd2b69cb698a166dc6d880fb550510d0144.jpeg) [@system](https://mne.discourse.group/u/system)\ **Post date:** [May 21, 2020, 7:41pm UTC](https://mne.discourse.group/t/time-frequency-analysis-questions/2190/2 "2020-05-21T19:41:47Z")
External Email - Use Caution Dear Kirill, looking at your plots you have clear edge artifacts. The function you use to do not try to correct for them automatically. You should crop your outputs in time and baseline afterwards. For pow I tend to baseline with a ratio between power during stim with power during baseline. Now to be honest this code is fairly old in MNE so maybe someone can suggest you a simpler route. I like this example if you are especially interested in power: [https://mne.tools/stable/auto\_examples/inverse/plot\_evoked\_ers\_source\_power.html?highlight=power](https://mne.tools/stable/auto_examples/inverse/plot_evoked_ers_source_power.html?highlight=power) HTH Alex ---
**Author:** ![system](https://global.discourse-cdn.com/free1/uploads/mne/original/1X/85cc6bd2b69cb698a166dc6d880fb550510d0144.jpeg) [@system](https://mne.discourse.group/u/system)\ **Post date:** [May 22, 2020, 5:02pm UTC](https://mne.discourse.group/t/time-frequency-analysis-questions/2190/3 "2020-05-22T17:02:11Z")
External Email - Use Caution Dear Dr. Gramfort, So, what is the recommended MNE code / functions to get rid of the edge artifacts and correct for other issues after I have obtained time-frequency plots? (I am interested in the source level data) So if source\_induced\_power() function does not automatically correct edge artificats, why it is achieved by parameters baseline, baseline\_mode there? It is already some correction, I would assume. Thank you in advance. Sincerely yours, Kirill Elin, PhD ??, 21 ??? 2020 ?. ? 22:42, Alexandre Gramfort \ : >         External Email - Use Caution > > Dear Kirill, > > looking at your plots you have clear edge artifacts. The function you use > to do not try to correct for them automatically. You should crop your > outputs > in time and baseline afterwards. > > For pow I tend to baseline with a ratio between power during stim > with power during baseline. > > Now to be honest this code is fairly old in MNE so maybe someone > can suggest you a simpler route. > > I like this example if you are especially interested in power: > > [Page Redirection](https://mne.tools/stable/auto_examples/inverse/plot_evoked_ers_source_power.html?highlight=power) > > HTH > Alex > > \> I need to do a time-frequency analysis and I am following this tutorial > [Page Redirection](https://mne.tools/dev/auto_examples/time_frequency/plot_source_label_time_frequency.html#sphx-glr-auto-examples-time-frequency-plot-source-label-time-frequency-py) > as I am interested in obtaining this sort of maps in certain labels. > However, the maps I am getting are definetely not normalized compared to > the examples (see my examples attached - lower than 10Hz). > \> 1. What might be the reason for this?Is there something special I need > to do when computing inverse solution compared to typical evoked response > analysis? (I include only noise covariance there) > \> 2. What is the recommend parameter here for baseline\_mode (percent, > logration mean - is there any suggestion on what to use and when?) > \> 3. In this tutorial and command source\_induced\_power, what is the method > used? Is it Morlet / Multitaper or something else? I found no information > on this in contrast to e.g. tfr\_morlet commands used elsewhere? > \> > \> Thank you in advance. > \> Sincerely yours, > \> Kirill Elin, PhD > \> > \> > \> \_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_ > \> Mne\_analysis mailing list > \> Mne\_analysis at nmr.mgh.harvard.edu > \> [Mne\_analysis Info Page](https://mail.nmr.mgh.harvard.edu/mailman/listinfo/mne_analysis) > > \_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_ > Mne\_analysis mailing list > Mne\_analysis at nmr.mgh.harvard.edu > [Mne\_analysis Info Page](https://mail.nmr.mgh.harvard.edu/mailman/listinfo/mne_analysis) -------------- next part -------------- An HTML attachment was scrubbed... URL: [http://mail.nmr.mgh.harvard.edu/pipermail/mne\_analysis/attachments/20200522/d2476c41/attachment.html](http://mail.nmr.mgh.harvard.edu/pipermail/mne_analysis/attachments/20200522/d2476c41/attachment.html) ---
**Author:** ![system](https://global.discourse-cdn.com/free1/uploads/mne/original/1X/85cc6bd2b69cb698a166dc6d880fb550510d0144.jpeg) [@system](https://mne.discourse.group/u/system)\ **Post date:** [May 22, 2020, 7:47pm UTC](https://mne.discourse.group/t/time-frequency-analysis-questions/2190/4 "2020-05-22T19:47:27Z")
External Email - Use Caution hi Kirill, I would suggest to try first the other approach I proposed to get power maps. [https://mne.tools/stable/auto\_examples/inverse/plot\_evoked\_ers\_source\_power.html?highlight=power](https://mne.tools/stable/auto_examples/inverse/plot_evoked_ers_source_power.html?highlight=power) using source\_induced\_power requires you to manipulate quite low level code. You would need to slice the numpy array and a time array accordingly. Alex