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Brain Source Localization using Constrained Low Rank Canonical Polyadic Decomposition

Abstract : A new tensor-based source localization algorithm is presented in this paper. It is a single-step algorithm in which tensor decomposition with an efficient rank estimation and source localization are performed in only one single step. Contrary to the previous single-step tensor-based STS-SISSY (Space-Time-Spike Source Imaging based on Structured Sparsity) method recently proposed by our group, the proposed method is robust to tensor over-factoring and gives more accurate results. In addition to the structural constraints on the sources required for their localization, group sparsity constraints on the loading over-estimated matrices of the constructed STS tensor is used to estimate its rank. The numerical results show the efficiency of the proposed method over the STS-SISSY one. © 2018 IEEE.
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Submitted on : Tuesday, April 2, 2019 - 4:31:24 PM
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N. Taheri, X. Han, Ahmad Karfoul, K. Ansari-Asl, I. Merlet, et al.. Brain Source Localization using Constrained Low Rank Canonical Polyadic Decomposition. 52nd Asilomar Conference on Signals, Systems and Computers, ACSSC 2018, Oct 2018, Pacific Grove, United States. pp.811-815, ⟨10.1109/ACSSC.2018.8645475⟩. ⟨hal-02088222⟩



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