134906 - Tensor decomposition for EEG signals retrieval.pdf (1.03 MB)
Tensor decomposition for EEG signals retrieval
conference contribution
posted on 2023-05-23, 14:13 authored by Cao, Z, Chang, Y-C, Prasad, M, Tanveer, M, Lin, C-TPrior 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.
History
Publication title
Proceedings of the 2019 IEEE International Conference on Systems, Man, and Cybernetics (IEEE SMC 2019)Pagination
1-5Department/School
School of Information and Communication TechnologyPublisher
Institute of Electrical and Electronics EngineersPlace of publication
United StatesEvent title
2019 IEEE International Conference on Systems, Man, and Cybernetics (IEEE SMC 2019)Event Venue
Bari, ItalyDate of Event (Start Date)
2019-10-06Date of Event (End Date)
2019-10-09Rights statement
Copyright 2019 IEEERepository Status
- Open