[Mne_analysis] computing peak frequency in source space

[Martin Luessi]

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_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-frequency-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/plot_dics_source_power.py
>
>     https://github.com/mne-tools/mne-python/blob/master/examples/inverse/plot_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

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