Event-related EEG power

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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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-R?mi

Very useful thanks.Last question: Is the Hilbert-based analysis
implemented in mne?

2018-01-16 16:25 GMT-06:00 JR KING <jeanremi.king at gmail.com>:

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
<mne.time_frequency.tfr_morlet — MNE 1.6.0 documentation;
and psd_multitaper
<https://mne-tools.github.io/stable/generated/mne.time_frequency.psd_multitaper.html#mne.time_frequency.psd_multitaper&gt;
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-R?mi

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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