Hello,
I'm wondering whether the measure of TF map (especially the "coefficient") in Brainstorm has some relationship with a "coherence definition" that I found in a reference paper (the following screenshot).
Since my research has something to do with frequency information, I need to make clear how do you measure TF map in Brainstorm. Any reference paper of your works would help a lot !
In Brainstorm, for a time series x(t), its TF map is a complex representations, let's call it X(t,f), this TF representation can be aggregated over the time axis, to obtain a complex spectral representation X(f).
The coefficient of a complex TF map, X(t,f), (or of a complex spectrum X(f)) is the magnitude of it abs(X).
Now, with respect to coherence, as you can see in the connectivity tutorial, the complex coherence between signals x and y, this is to say C_xy(f) is computed from the cross-spectra S_xy(f) between those signals, and their respective power spectrum S_xx(f) and Syy(f). Finally, S_xx(f) is equal to the square of the coefficient (magnitude) of the complex spectra of X:
S_xx(f) = |X(f)|^2
A post would be very short to go through all the details on these topics, you can check these reference (one is a shameless own work) for further information.
Nolte G, Bai O, Wheaton L, Mari Z, Vorbach S, Hallett M.
Identifying true brain interaction from EEG data using the imaginary part of coherency.
Clinical Neurophysiology. 2004 Oct;115(10):2292–307.
Cassani, R. & Falk, T. H. (2018)
Spectrotemporal Modeling of Biomedical Signals: Theoretical Foundation and Applications. in Reference Module in Biomedical Sciences (Elsevier, 2018).
Vinck M, Oostenveld R, van Wingerden M, Battaglia F, Pennartz CM
An improved index of phase-synchronization for electrophysiological data in the presence of volume-conduction, noise and sample-size bias
Neuroimage, Apr 2011
