[FieldTrip] Source localization of resting state EEG data using lcmv beamformer

Honcamp, Hanna (PSYCHOLOGY) h.honcamp at maastrichtuniversity.nl
Thu Feb 24 21:00:39 CET 2022

Dear Fieldtrip community,


I am analyzing continuous EEG resting state (RS) data and I have a couple of questions about the beamformer source reconstruction methods and the best application of it in this context. Firstly, I have not seen many tutorials/papers concerned with RS source analysis using the lcmv beamforming method. Since I am interested in the analysis and properties of the reconstructed source time courses, the lcmv seemed a viable option. Further, in van Veen et al. (1997), it is described that the computation of the covariance matrix assumes that the data is "wide sense stationary". This description seems to be contradictory to the non-stationarity of RS data.

Q1: Is the lcmv beamformer the recommended source analysis method for resting state data for the purpose of extracting the source time courses?

Q2: What is the best way to compute the covariance matrix in the context of RS data? Specifically, should I use the whole data or a subset of timepoints? What is the reasoning behind that?

Lastly, I understood that the common filter approach is recommended for within-subject analysis, e.g., comparing conditions. However, it is not clear whether the common filter is also feasible to use in a multi-subject context.

Q3: In order to compare the reconstructed source time courses of multiple subjects, do I need to construct a common filter and apply it to all subjects? If so, how does that affect the covariance computation, i.e., should I use all subjects (e.g., in a concatenated format) for computation of the covariance matrix, or a subset of subjects?

Many thanks in advance - any advice is much appreciated!





Hanna Honcamp
PhD Candidate | BAND Lab
Faculty of Psychology and Neuroscience
Dept. NP&PP | Maastricht University
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