Fig. 1
From: Local dimension-reduced dynamical spatio-temporal models for resting state network estimation
The main algorithm consists of (i) the application of a sparsifying spatial wavelet transformation, resulting into a description in terms of wavelet coefficient time series, (ii) contiguity-constrained clustering of the time series of wavelet coefficients by grouping only nearby coefficients and (iii) estimation of the observation matrix and system states by linear dimensionality reduction of the identified clusters