Canonical Polyadic Decomposition of EEG Image Tensor for BCI Applications

Author(s):  
K. Keerthi Krishnan ◽  
K. P. Soman
2019 ◽  
Vol 2019 ◽  
pp. 1-8
Author(s):  
Long Liu ◽  
Ling Wang ◽  
Yuexian Wang ◽  
Jian Xie ◽  
Zhaolin Zhang

The problem of parameter estimation of coherent signals impinging on an array with vector sensors is considered from a new perspective by means of the decomposition of tensors. Signal parameters to be estimated include the direction of arrival (DOA) and the state of polarization. In this paper, mild deterministic conditions are used for canonical polyadic decomposition (CPD) of the tensor-based signal model; i.e., the factor matrices can be recovered, as long as the matrices satisfy the requirement that at least one is full column rank. In conjoint with the estimation of signal parameters via the algebraic method, the DOAs and polarization parameters of coherent signals can be resolved by virtue of the first and second factor matrices. Hereinto, the key innovation of the proposed approach is that the proposed approach can effectively estimate the coherent signal parameters without sacrificing the array aperture. The superiority of the proposed algorithm is shown by comparing with the algorithms based on higher order singular value decomposition (HOSVD) and Toeplitz matrix. Theoretical and numerical simulations demonstrate the effectiveness of the proposed approach.


2021 ◽  
Vol 5 (2) ◽  
pp. 605-610
Author(s):  
Ahmed S. Zamzam ◽  
Yajing Liu ◽  
Andrey Bernstein

2020 ◽  
Vol 39 (4) ◽  
pp. 844-853
Author(s):  
Li-Dan Kuang ◽  
Qiu-Hua Lin ◽  
Xiao-Feng Gong ◽  
Fengyu Cong ◽  
Yu-Ping Wang ◽  
...  

2020 ◽  
Vol 31 (6) ◽  
pp. 2174-2188 ◽  
Author(s):  
Anh-Huy Phan ◽  
Andrzej Cichocki ◽  
Ivan Oseledets ◽  
Giuseppe G. Calvi ◽  
Salman Ahmadi-Asl ◽  
...  

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