[Mne_analysis] Hilbert Transform for Time Frequency Analysis

[Chris Holdgraf]

Just to clarify a bit - you can use the Hilbert transform for a TFR
representation by doing:

   1. Bandpass filter your data (e.g., raw.filter)
   2. Call apply_hilbert
      1. If envelope=False, the output will be complex, so you can grab
      either the phase or amplitude if you want
      2. If envelope=True, the output will be a float and represents the
      envelope of the signal (e.g. the “power” of a TFR)

​
-- 

On Tue, Jul 4, 2017 at 12:20 PM Alday, Phillip <Phillip.Alday at mpi.nl> wrote:

> The Raw class has a method "apply_hilbert()" that you can use, but
> you'll have to take care of bandpassing, iterating, etc. to get it the
> way you want it, something along the lines of the CSP example in the
> documentation (http://martinos.org/mne/dev/auto_examples/decoding/plot_
> decoding_csp_timefreq.html#sphx-glr-auto-examples-decoding-plot-
> <http://martinos.org/mne/dev/auto_examples/decoding/plot_decoding_csp_timefreq.html#sphx-glr-auto-examples-decoding-plot->
> decoding-csp-timefreq-py). The only "difficult" part is if you want the
> phase instead of the envelope/amplitude, in which case you'll have to
> do the phase transform yourself. (Maybe it's worthwhile to add a phase
> option to the method?)
>
> But done appropriately, you get the same answers with Hilbert, Fourier
> or wavelets (Bruns, J Neuroscience Methods 2004).
>
> Phillip
>
> On Tue, 2017-07-04 at 21:46 +0530, Aditya P Singh wrote:
> > Hi,
> >
> > I was wondering if there was anyway to do the time frequency analysis
> > like getting the power and building the tfr plots using the hilbert
> > transform instead of the morelet wavelets.
> >
> > Thank you,
> > Aditya Singh
> > Biomedical Engineer
> > UT Austin
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