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Tensor decomposition for EEG signals retrieval

Citation

Cao, Z and Chang, Y-C and Prasad, M and Tanveer, M and Lin, C-T, Tensor decomposition for EEG signals retrieval, Proceedings of the 2019 IEEE International Conference on Systems, Man, and Cybernetics (IEEE SMC 2019), 06-09 October 2019, Bari, Italy, pp. 1-5. (2019) [Refereed Conference Paper]


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Copyright Statement

Copyright 2019 IEEE

DOI: doi:10.1109/SMC.2019.8914076

Abstract

Prior studies have proposed methods to recover multi-channel electroencephalography (EEG) signal ensembles from their partially sampled entries. These methods depend on spatial scenarios, yet few approaches aiming to a temporal reconstruction with lower loss. The goal of this study is to retrieve the temporal EEG signals independently which was overlooked in data pre-processing. We considered EEG signals are impinging on tensor-based approach, named nonlinear Canonical Polyadic Decomposition (CPD). In this study, we collected EEG signals during a resting-state task. Then, we defined that the source signals are original EEG signals and the generated tensor is perturbed by Gaussian noise with a signal-to-noise ratio of 0 dB. The sources are separated using a basic nonnegative CPD and the relative errors on the estimates of the factor matrices. Comparing the similarities between the source signals and their recovered versions, the results showed significantly high correlation over 95%. Our findings reveal the possibility of recoverable temporal signals in EEG applications.

Item Details

Item Type:Refereed Conference Paper
Keywords:EEG, tensor, nonlinear, CPD, recovery, tensor decomposition
Research Division:Information and Computing Sciences
Research Group:Machine learning
Research Field:Neural networks
Objective Division:Defence
Objective Group:Defence
Objective Field:Intelligence, surveillance and space
UTAS Author:Cao, Z (Dr Zehong Cao)
ID Code:134906
Year Published:2019
Deposited By:Information and Communication Technology
Deposited On:2019-09-11
Last Modified:2020-06-16
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