<div dir="ltr">Very useful thanks.Last question: Is the Hilbert-based analysis implemented in mne? </div><div class="gmail_extra"><br><div class="gmail_quote">2018-01-16 16:25 GMT-06:00 JR KING <span dir="ltr"><<a href="mailto:jeanremi.king@gmail.com" target="_blank">jeanremi.king@gmail.com</a>></span>:<br><blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex"><div dir="ltr"><div>Dear Mat,<br><br></div>check out our time frequency tutorial in sensor space:<br><a href="https://mne-tools.github.io/stable/auto_tutorials/plot_sensors_time_frequency.html" target="_blank">https://mne-tools.github.io/<wbr>stable/auto_tutorials/plot_<wbr>sensors_time_frequency.html</a><br><br>In addition,<br> <a href="https://mne-tools.github.io/stable/generated/mne.time_frequency.tfr_morlet.html#mne.time_frequency.tfr_morlet" title="View documentation for mne.time_frequency.tfr_morlet" target="_blank"><span class="m_6122070955036712452gmail-n">tfr_morlet</span></a> and <a href="https://mne-tools.github.io/stable/generated/mne.time_frequency.psd_multitaper.html#mne.time_frequency.psd_multitaper" title="View documentation for mne.time_frequency.psd_multitaper" target="_blank"><span class="m_6122070955036712452gmail-n">psd_multitaper</span></a> apply a time frequency decomposition on Epochs data or Evoked data (i.e. epochs.average().<br><pre><br>To get evoked power, just do tfr_morlet(evoked)<br>To get induced power, just to the subtraction between tfr_morlet(epochs) and tfr_morlet(evoked)<br><br><br>Time frequency decomposition methods (wavelets, multitaper, etc) are generally highly converging. I don't think there is a clear consensus on whether one should be favored for your particular case.<br><br>Hope that helps,<br><br>Jean-Rémi<br></pre></div><div class="gmail_extra"><br><div class="gmail_quote"><div><div class="h5">On 16 January 2018 at 16:51, Servant Mathieu <span dir="ltr"><<a href="mailto:servant.mathieu@gmail.com" target="_blank">servant.mathieu@gmail.com</a>></span> wrote:<br></div></div><blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex"><div><div class="h5"><div dir="ltr">Hi,<div><br></div><div>I'd like to perform different types of event-related power analyses from my EEG data: </div><div>-total power (magnitude of oscillations irrespective of their phase angles)</div><div>-evoked power (changes in EEG power that are phase-locked with respect to the event onset across trials)</div><div>-induced power (event-related changes in EEG power that are time-locked, but not phase-locked with respect to the event onset)</div><div><br></div><div>I am relatively new to time-frequency analyses. Is there some sort of tutorial in mne? For this particular project, I am mainly interested in the theta frequency band. Which time-frequency decomposition method (STFT, Hilbert wavelet..) would you recommend? Any example code would be appreciated!</div><div><br></div><div>Best,</div><div>Mat</div></div>
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