[Mne_analysis] computing peak frequency in source space

[Luke Bloy]

Is there a reason that compute_source_psd doesn't support MNE or is this
just a bug?

The error I get comes from line 429 of time_frequency.py. basically
noise_norm is None so noise_norm.size errors.

AttributeError: 'NoneType' object has no attribute 'size'

and in _assemble_kernel (inverse.py line 669) noise_norm is set to None if
the method = 'MNE'

it seems like line 429 is just trying to get the number of sources?
Couldn't we get that from K? or am i missing something?

Thanks,
Luke


On Mon, Sep 30, 2013 at 12:55 PM, Martin Luessi <mluessi at nmr.mgh.harvard.edu
> wrote:

> 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<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<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<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_source_power.py>
>>
>>     https://github.com/mne-tools/**mne-python/blob/master/**
>> examples/inverse/plot_dics_**beamformer.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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