Tensor decomposition for EEG signals retrieval
Date
2019
Authors
Cao, Z.
Chang, Y.C.
Prasad, M.
Tanveer, M.
Lin, C.T.
Editors
Advisors
Journal Title
Journal ISSN
Volume Title
Type:
Conference paper
Citation
Conference proceedings / IEEE International Conference on Systems, Man, and Cybernetics. IEEE International Conference on Systems, Man, and Cybernetics, 2019, vol.2019-October, pp.2423-2427
Statement of Responsibility
Conference Name
IEEE International Conference on Systems, Man and Cybernetics (SMC) (6 Oct 2019 - 9 Oct 2019 : ITALY, Bari)
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
School/Discipline
Dissertation Note
Provenance
Description
Link to a related website: https://unpaywall.org/10.1109/SMC.2019.8914076, Open Access via Unpaywall
Access Status
Rights
Copyright 2019 IEEE