I think my main question for the moment would be: which method / pipeline
would be the most appropriate?
Looking into the literature, it seems like DICS is most used, probably
because normally you are interested in some form of oscillatory component,
and DICS uses the cross-spectral density matrix
Interestingly, tf_dics seems to be the only source localization / inverse
calculation that accept Epochs as parameter (although it calculates the csd
for each epoch, averages afterwards and return one source localization for
the average)
All the other methods expect Evoked data
So a more specific question would be:* How can I source localize each epoch
using for instance mne.inver_sparse, perform a PSD/TF tranformation in*
*source space for each epoch, and then average the results? **Is there some
example in this sense?*
There is an interesting post by Matti Hamalainen about that
And taking into consideration the other posts and all the technical
aspects, I would prefer to apply the TF in source space and avoid
introducing temporal correlations in the signal if possible.
Btw, can I make a suggestion? Change the name in the main menu from
"Gallery" to "Examples"!
I was mostly looking at Tutorials, imagining that Gallery was something
like figures and print-screens
Thank you!
Leonardo
2016-11-03 8:40 GMT-05:00 Alexandre Gramfort <alexandre.gramfort at telecom-
paristech.fr>:
hi Leonardo,
I will not comment on what's best but you can use all linear inverse
solvers
(beamformers, MNE/dSPM/sLORETA) on epochs or even raw data.
I think my main question for the moment would be: which method / pipeline
would be the most appropriate?
Looking into the literature, it seems like DICS is most used, probably
because normally you are interested in some form of oscillatory component,
and DICS uses the cross-spectral density matrix
Interestingly, tf_dics seems to be the only source localization / inverse
calculation that accept Epochs as parameter (although it calculates the csd
for each epoch, averages afterwards and return one source localization for
the average)
All the other methods expect Evoked data
So a more specific question would be:* How can I source localize each
epoch using for instance mne.inver_sparse, perform a PSD/TF tranformation
in*
*source space for each epoch, and then average the results? **Is there
some example in this sense?*
There is an interesting post by Matti Hamalainen about that
And taking into consideration the other posts and all the technical
aspects, I would prefer to apply the TF in source space and avoid
introducing temporal correlations in the signal if possible.
Thank you in advance for any help!
Leonardo
------------------------
Leonardo S. Barbosa, PhD
Postdoctoral Research Scientist
University of Wisconsin, Madison
Center for Sleep and Consciousness Studies
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Dear all
I would appreciate it if you would kindly provide me with the correct
link to the reference the MNE sample data set. I am hoping to submit a
paper, using that the data set. It is important that I cite the correct
paper or make the correct reference to it.
Many thanks
best regards parham hashemzadeh
the MNE sample dataset was acquired to be used in the manual and the tutorials.
please use the reference to the MNE soft:
A. Gramfort, M. Luessi, E. Larson, D. Engemann, D. Strohmeier, C.
Brodbeck, L. Parkkonen, M. H?m?l?inen, MNE software for processing MEG
and EEG data, NeuroImage, Volume 86, 1 February 2014, Pages 446-460,
ISSN 1053-8119,
