[Mne_analysis] Event-related EEG power

[JR KING]

Dear Mat,

check out our time frequency tutorial in sensor space:
https://mne-tools.github.io/stable/auto_tutorials/plot_sensors_time_frequency.html

In addition,
 tfr_morlet
<https://mne-tools.github.io/stable/generated/mne.time_frequency.tfr_morlet.html#mne.time_frequency.tfr_morlet>
and psd_multitaper
<https://mne-tools.github.io/stable/generated/mne.time_frequency.psd_multitaper.html#mne.time_frequency.psd_multitaper>
apply a time frequency decomposition on Epochs data or Evoked data (i.e.
epochs.average().


To get evoked power, just do tfr_morlet(evoked)
To get induced power, just to the subtraction between
tfr_morlet(epochs) and tfr_morlet(evoked)


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.

Hope that helps,

Jean-Rémi


On 16 January 2018 at 16:51, Servant Mathieu <servant.mathieu at gmail.com>
wrote:

> Hi,
>
> I'd like to perform different types of event-related power analyses from
> my EEG data:
> -total power (magnitude of oscillations irrespective of their phase angles)
> -evoked power (changes in EEG power that are phase-locked with respect to
> the event onset across trials)
> -induced power (event-related changes in EEG power that are time-locked,
> but not phase-locked with respect to the event onset)
>
> 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!
>
> Best,
> Mat
>
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