[Mne_analysis] ERD/ERS on source space data
Thomas Hartmann
thomas.hartmann at th-ht.de
Thu Oct 11 09:30:29 EDT 2018
External Email - Use Caution
hi,
i faced a similar question some time ago. there is one thing to keep in
mind when doing (time)frequency transforms on source data:
when data is projected to source space, it is projected to three
components per source: one for each spatial dimension. these three
components are then normally combined by taking the norm. this step
includes squaring the data, so all samples that were negative are now
positive. this is mostly ok when staying in the time-domain (although
one must be aware of it, but it will not mess up anything). however, it
essentially doubles the frequencies of all oscillations in your data, so
it is highly problematic for (time)frequency transforms.
mne python provides a few ways to avoid this problem:
1. use source_induced_power. this function calculates the TFR for all
three components of every source and combines the power values
afterwards.
2. restrict the amount of spatial components per source to 1 when
setting up the source model. i do not know if that is possible in
mne python.
3. restrict apply_inverse to return only one component.
pick_ori='normal' would work well for EEG but not for MEG, though
because it is mostly sensitive to the two tangential components.
4. set pick_ori to 'vector' which should return all three spatial
components per source, apply whatever algorithm(s) you need and
combine them later yourself.
i hope, this is all correct. if not, please correct me!
cheers,
thomas
Am 11.10.18 um 15:04 schrieb Sheraz Khan, PhD:
> Yes, in addition, once you extract the source space data from the label,
> you can also use tfr_multitaper on the label time series.
>
> HTH
>
> Sheraz
>
>
>> External Email - Use Caution
>>
>> You mean basically using source_induced_power instead o tfr_multitaper?
>>
>> Thanks for answering,
>> Karin
>>
>> Den tors 11 okt. 2018 kl 00:55 skrev Sheraz Khan, PhD <
>> sheraz at nmr.mgh.harvard.edu>:
>>
>>> Hi Karin,
>>>
>>> Yes, you need to pick few labels in the source space to estimate ERS/ESD
>>> on it.
>>>
>>> Please look at the following example:
>>>
>>>
>>> https://martinos.org/mne/stable/auto_examples/time_frequency/plot_source_label_time_frequency.html
>>>
>>> Thanks
>>>
>>> Sheraz
>>>
>>>
>>>> External Email - Use Caution
>>>>
>>>> Hi!
>>>>
>>>> Is it possible to compute ERS/ERD maps based on source space instead
>>> of
>>>> sensor space?
>>>>
>>>> Best,
>>>> Karin
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>>>
>>> -------------------------
>>> Sheraz Khan, M.Eng, Ph.D.
>>> Instructor in Radiology
>>>
>>> Athinoula A. Martinos Center for Biomedical Imaging
>>> Massachusetts General Hospital
>>> Harvard Medical School
>>>
>>> McGovern Institute for Brain Research
>>> Massachusetts Institute of Technology
>>>
>>> Tel: +1 617-643-5634
>>> Fax: +1 617-948-5966
>>> Email: sheraz at nmr.mgh.harvard.edu
>>> sheraz at mit.edu
>>> Web: http://sheraz.mit.edu
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>
> -------------------------
> Sheraz Khan, M.Eng, Ph.D.
> Instructor in Radiology
>
> Athinoula A. Martinos Center for Biomedical Imaging
> Massachusetts General Hospital
> Harvard Medical School
>
> McGovern Institute for Brain Research
> Massachusetts Institute of Technology
>
> Tel: +1 617-643-5634
> Fax: +1 617-948-5966
> Email: sheraz at nmr.mgh.harvard.edu
> sheraz at mit.edu
> Web: http://sheraz.mit.edu
> _______________________________________________
> Mne_analysis mailing list
> Mne_analysis at nmr.mgh.harvard.edu
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--
Dr. Thomas Hartmann
Centre for Cognitive Neuroscience
FB Psychologie
Universität Salzburg
Hellbrunnerstraße 34/II
5020 Salzburg
Tel: +43 662 8044 5109
Email: thomas.hartmann at th-ht.de
"I am a brain, Watson. The rest of me is a mere appendix. " (Arthur Conan Doyle)
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