[Mne_analysis] computing peak frequency in source space

Denis-Alexander Engemann denis.engemann at gmail.com
Mon Sep 30 13:10:22 EDT 2013
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Sorry Luke,

I did not get what you were looking for when reading your mail first. I'm
glad the example Martin posted does what you were looking for.

> so most of the inverse operator code in python runs into
> memory problems.

By the way when working with resting state data you can also generate epochs
using `mne.event.make_fixed_length_events` and compute source power on
epochs using `mne.minimum_norm.compute_source_psd_epochs` with the option
`return_generator` set to True. This will avoid loading all data into
memory by not returning a list of source estimates but a generator you can
unfold in a simple loop, such that only one source estimate is processed at
a given iteration.
Or you can pass it to functions that do the unpacking as required.

See this example:
http://martinos.org/mne/auto_examples/connectivity/plot_mne_inverse_coherence_epochs.html#example-connectivity-plot-mne-inverse-coherence-epochs-py


Cheers,
Denis


On Mon, Sep 30, 2013 at 6:54 PM, Luke Bloy <luke.bloy at gmail.com> wrote:

> That is exactly what I was looking for. Thanks.
>
> Best,
> Luke
>
> -----Original Message-----
> From: Martin Luessi [mailto:mluessi at nmr.mgh.harvard.edu]
> Sent: Monday, September 30, 2013 12:55 PM
> To: Luke Bloy
> Cc: Denis-Alexander Engemann; mne_analysis at nmr.mgh.harvard.edu
> Subject: Re: [Mne_analysis] computing peak frequency in source space
>
> Hi Luke,
>
> I think you want to either use compute_source_psd or (for raw data) or
> compute_source_psd_epochs (for epoched data). The functions are in
> mne.minimum_norm.time_frequency. There is an example here:
>
>
> http://martinos.org/mne/auto_examples/time_frequency/plot_source_power_spect
> rum.html<http://martinos.org/mne/auto_examples/time_frequency/plot_source_power_spectrum.html>
>
> I hope this helps.
>
> Martin
>
> On 09/30/13 12:41, Luke Bloy wrote:
> > Thanks for the quick reply Denis.
> >
> > I'm interested in the peak frequency within a band not the power
> > timecourse of the band so source_band_induced_power isn't what I want.
> > I also want to stick with MNE, as opposed to DICS or another
> > beamformer, for localization since I will want to compare with mne/dspm
> power estimates.
> >
> > The other complicating factor is that I'm using resting state data (~~
> > 5 minutes), so most of the inverse operator code in python runs into
> > memory problems. Otherwise I could just do apply_inverse and then work
> > on the returned timecourses in the stc.
> >
> > once I have a time course for each source finding the peak frequency
> > will be pretty straight forward using numpy.fft and numpy.fft.fftfreq.
> >
> > Hope this makes sense
> > -Luke
> >
> >
> >
> > On Mon, Sep 30, 2013 at 12:14 PM, Denis-Alexander Engemann
> > <denis.engemann at gmail.com <mailto:denis.engemann at gmail.com>> wrote:
> >
> >     Hi Luke,
> >
> >
> >     On Mon, Sep 30, 2013 at 6:02 PM, Luke Bloy <luke.bloy at gmail.com
> >     <mailto:luke.bloy at gmail.com>> wrote:
> >
> >         Hi all,
> >
> >         I am interested in computing the peak frequency within a band
> >         for each source.
> >
> >         So my first question is does this already exists somewhere?
> >
> >
> >
> >     This example might be of interest.
> >
> >
> > http://martinos.org/mne/auto_examples/time_frequency/plot_source_space
> > _time_frequency.html#example-time-frequency-plot-source-space-time-fre
> > quency-py
> >
> >     Basically it returns source estimates per frequency band each of
> >     which can be visualized on e.g. a cortical surface.
> >
> >     Another timely alternative is the DICS bearmformer recently added by
> >     Roman:
> >
> >
> > https://github.com/mne-tools/mne-python/blob/master/examples/inverse/p
> > lot_dics_source_power.py
> >
> >
> > https://github.com/mne-tools/mne-python/blob/master/examples/inverse/p
> > lot_dics_beamformer.py
> >
> >     you can always use numpy.argmax and argsort functions to quickly
> >     navigate through peaks inside the resulting arrays.
> >
> >
> >         If not what would people suggest as a jumping off point for
> >         developing it. I was thinking of following apply_inverse in
> >         inverse.py until I get the final inverse operator (K in line
> >         753) and then looping through each row (source) in K to compute
> >         the time course and peak power and frequency. Any other
> >         suggestions or downsides to this approach?
> >
> >
> >     Maybe let's first see whether what is implemented so far gives you
> >     what you're looking for.
> >
> >     I hope this helps + cheers,
> >     Denis
> >
> >         Thanks,
> >         Luke
> >
> >
> >
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>
> --
> Martin Luessi, Ph.D.
>
> Research Fellow
>
> Department of Radiology
> Athinoula A. Martinos Center for Biomedical Imaging Massachusetts General
> Hospital Harvard Medical School
> 149 13th Street
> Charlestown, MA 02129
>
> Fax: +1 617 726-7422
>
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