Geodesic and functional K-means algorithms
نویسندگان
چکیده
We propose a method based on the K-means algorithm to recover correlated areas from MEG/EEG reconstructions at the source level. We use a mixt distance based on both anatomical and functional information in order to parcellise the cortex in several areas. We restrict our distance so that parcels are always of small extent and low dimensionality, and so that local correlations can be computed more precisely. We finally select sources of interest through a multistart procedure and find the relevant networks. We applied this method on both simulated data and real recordings from a visual task.
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