reconstruct virtual MEG channels in source-space

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Hi all,

??? I have used the dSPM algorithm to get the maximum activation point. I want to do a time-frequency analysis of the data from this maximum activation point.
So I need to reconstruct the virtual channel data for this maximum activation point, which means I can get the epoch data about this vertice. This method has mentioned in fieldtrip(http://www.fieldtriptoolbox.org/tutorial/virtual_sensors/), but I don't know how to make it by MNE. Someone told me that the 'mne.minimum_norm.apply_inverse_epochs' function can do it, but I don't know how to do it specifically. This function returns the stc file, which only tells me how much activation each point has at each time point.

Best wishes,

Eric

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hi Eric,

please have a look at:

https://martinos.org/mne/dev/auto_examples/inverse/plot_compute_mne_inverse_epochs_in_label.html
<https://martinos.org/mne/dev/auto_examples/inverse/plot_compute_mne_inverse_epochs_in_label.html#sphx-glr-auto-examples-inverse-plot-compute-mne-inverse-epochs-in-label-py>

using apply_inverse_epochs you can get time courses of a label / vertice in
the brain over epochs.
you can then pass this tfr_* functions.

this should hopefully help.

if you want look at power in source space you can also use DICS:

https://martinos.org/mne/stable/auto_examples/inverse/plot_dics_source_power.html

Alex