{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,19]],"date-time":"2026-01-19T06:14:37Z","timestamp":1768803277545,"version":"3.49.0"},"reference-count":55,"publisher":"World Scientific Pub Co Pte Lt","issue":"04","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Int. J. Neur. Syst."],"published-print":{"date-parts":[[2015,6]]},"abstract":"<jats:p> For M\/EEG-based distributed source imaging, it has been established that the L<jats:sub>2<\/jats:sub>-norm-based methods are effective in imaging spatially extended sources, whereas the L<jats:sub>1<\/jats:sub>-norm-based methods are more suited for estimating focal and sparse sources. However, when the spatial extents of the sources are unknown a priori, the rationale for using either type of methods is not adequately supported. Bayesian inference by exploiting the spatio-temporal information of the patch sources holds great promise as a tool for adaptive source imaging, but both computational and methodological limitations remain to be overcome. In this paper, based on state-space modeling of the M\/EEG data, we propose a fully data-driven and scalable algorithm, termed STRAPS, for M\/EEG patch source imaging on high-resolution cortices. Unlike the existing algorithms, the recursive penalized least squares (RPLS) procedure is employed to efficiently estimate the source activities as opposed to the computationally demanding Kalman filtering\/smoothing. Furthermore, the coefficients of the multivariate autoregressive (MVAR) model characterizing the spatial-temporal dynamics of the patch sources are estimated in a principled manner via empirical Bayes. Extensive numerical experiments demonstrate STRAPS's excellent performance in the estimation of locations, spatial extents and amplitudes of the patch sources with varying spatial extents. <\/jats:p>","DOI":"10.1142\/s0129065715500161","type":"journal-article","created":{"date-parts":[[2015,3,13]],"date-time":"2015-03-13T01:19:09Z","timestamp":1426209549000},"page":"1550016","source":"Crossref","is-referenced-by-count":22,"title":["STRAPS: A Fully Data-Driven Spatio-Temporally Regularized Algorithm for M\/EEG Patch Source Imaging"],"prefix":"10.1142","volume":"25","author":[{"given":"Ke","family":"Liu","sequence":"first","affiliation":[{"name":"College of Automation Science and Engineering, South China University of Technology, Guangzhou 510641, P. R. 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