[Mne_analysis] Multitapers and DICS

[Andrea Brovelli]

Thanks !

Andrea


Le 26/05/2016 à 03:14, Alexandre Gramfort a écrit :
> hi Andrea,
>
> thanks for clarifying. I created an issue on github:
>
> https://github.com/mne-tools/mne-python/issues/3249
>
> hopefully someone will look into it soon.
>
> Alex
>
>
> On Wed, May 25, 2016 at 9:31 AM, Andrea Brovelli
> <andrea.brovelli at univ-amu.fr> wrote:
>> Hi Alexandre,
>> yes, exactly.
>> In order to compute the single-trial power at the source level at a single
>> frequency, the steps I normally use (in Fieldtrip) are:
>> 1) Multitaper at the sensor level: compute complex-valued estimates of
>> spectral measures Xsensor(n, t, k) , for each trial n, time t, and taper k.
>> The cross-spectral density matrices, however, is computed across trials
>> (average csd).
>> 2) DICS: compute complex-value spectral measures at the source level,
>> Xsource(s, t, k) =  A * Xsensor(n, t, k) , where A(t) is the real-valued
>> spatial filter that transforms the data from the sensor to source level,
>> which is computed from the real part of the csd in (1). The Xsource(s, t, k)
>> is calculated for each source s, time t, and taper k
>> 3) The single-trial power at each source location is computed by multiplying
>> the complex spectral estimates with their complex conjugate, and averaged
>> over tapers k. Psource(s, t) = <   Xsource dot Xsource(s, t, k) * > over k
>> 4) Finally, I log-transform the power values to make the data approximate
>> Gaussian and lowpass filtered at 50 Hz to reduce noise. Then, the
>> single-trial mean power and SD in the baseline period (i.e., multiple
>> estimates of noise) are computed for each source and trial, and used to
>> z-transform single-trial event-related power time courses.
>>
>> I hope this is clear. If you want a detailed description you can read the
>> paragraph "Single-trial HGA at BAs" in the Methods in this paper:
>> http://www.jneurosci.org/content/35/37/12643.short
>>
>> However, I am not familiar with MNE yet, I am slowly learning. So, I don't
>> know exactly how I could contribute to implement this. But I can help ! Just
>> let me know.
>>
>> bye
>> Andrea
>>
>>
>> Le 25/05/2016 à 13:46, Alexandre Gramfort a écrit :
>>
>> hi Andrea,
>>
>> hum I think you're right.
>>
>> If I follow what you're saying we should have a noise_csds per
>> frequency and per taper. Then compute the STCs for each taper and then
>> average. Is that what you have in mind?
>>
>> Alex
>>
>> On Tue, May 24, 2016 at 6:21 AM, Andrea Brovelli
>> <andrea.brovelli at univ-amu.fr> wrote:
>>
>> Dear all,
>> I have a question regarding the use of multitapers and DICS.
>> As far as I know, the power spectral density using multitaper is the mean
>> power averaged across tapers (e.g., equation 6 in the paper from Mitra and
>> Pesaran http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1300074/pdf/9929474.pdf)
>> At the source level, using DICS for example, I assume that we should compute
>> the power for each taper and then average across tapers to get the mean
>> power estimate for each source.
>> While looking at the example on "Compute source power using DICS beamfomer"
>> (http://martinos.org/mne/stable/auto_examples/inverse/plot_dics_source_power.html#sphx-glr-auto-examples-inverse-plot-dics-source-power-py),
>> however it looks like that you compute the mean cross-spectral density
>> averaged across tapers (which has complex values), and then you use it to
>> compute the power at the source level.
>> Is that the case ?
>> If yes, would it be possible to modify it so to:
>> 1) keep the csd for each taper
>> 2) apply the spatial filter for each taper to the channel data to compute
>> the source power for each taper
>> 3) average source power across tapers
>>
>> Here are the lines of code I refer to in the Example
>>
>> # Computing the data and noise cross-spectral density matrices
>> # The time-frequency window was chosen on the basis of spectrograms from
>> # example time_frequency/plot_time_frequency.py
>> # As fsum is False compute_epochs_csd returns a list of CrossSpectralDensity
>> # instances than can then be passed to dics_source_power
>> data_csds = compute_epochs_csd(epochs, mode='multitaper', tmin=0.04,
>> tmax=0.15,
>>                                 fmin=15, fmax=30, fsum=False)
>> noise_csds = compute_epochs_csd(epochs, mode='multitaper', tmin=-0.11,
>>                                  tmax=-0.001, fmin=15, fmax=30, fsum=False)
>>
>> # Compute DICS spatial filter and estimate source power
>> stc = dics_source_power(epochs.info, forward, noise_csds, data_csds)
>>
>>
>> Thanks a lot,
>> Best,
>> Andrea
>>
>> _______________________________________________
>> Mne_analysis mailing list
>> Mne_analysis at nmr.mgh.harvard.edu
>> https://mail.nmr.mgh.harvard.edu/mailman/listinfo/mne_analysis
>>
>>
>> The information in this e-mail is intended only for the person to whom it is
>> addressed. If you believe this e-mail was sent to you in error and the
>> e-mail
>> contains patient information, please contact the Partners Compliance
>> HelpLine at
>> http://www.partners.org/complianceline . If the e-mail was sent to you in
>> error
>> but does not contain patient information, please contact the sender and
>> properly
>> dispose of the e-mail.
>>
>> _______________________________________________
>> Mne_analysis mailing list
>> Mne_analysis at nmr.mgh.harvard.edu
>> https://mail.nmr.mgh.harvard.edu/mailman/listinfo/mne_analysis
>>
>>
>>
>> _______________________________________________
>> Mne_analysis mailing list
>> Mne_analysis at nmr.mgh.harvard.edu
>> https://mail.nmr.mgh.harvard.edu/mailman/listinfo/mne_analysis
>>
>>
>> The information in this e-mail is intended only for the person to whom it is
>> addressed. If you believe this e-mail was sent to you in error and the
>> e-mail
>> contains patient information, please contact the Partners Compliance
>> HelpLine at
>> http://www.partners.org/complianceline . If the e-mail was sent to you in
>> error
>> but does not contain patient information, please contact the sender and
>> properly
>> dispose of the e-mail.
>>
> _______________________________________________
> Mne_analysis mailing list
> Mne_analysis at nmr.mgh.harvard.edu
> https://mail.nmr.mgh.harvard.edu/mailman/listinfo/mne_analysis