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Tensor decomposition for EEG signals retrieval
- Source :
- SMC
- Publication Year :
- 2019
- Publisher :
- IEEE, 2019.
-
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 usc Refereed/Peer-reviewed
- Subjects :
- Signal Processing (eess.SP)
Computer science
02 engineering and technology
Electroencephalography
Signal
03 medical and health sciences
symbols.namesake
Matrix (mathematics)
0302 clinical medicine
FOS: Electrical engineering, electronic engineering, information engineering
0202 electrical engineering, electronic engineering, information engineering
medicine
Tensor decomposition
EEG
Tensor
Electrical Engineering and Systems Science - Signal Processing
medicine.diagnostic_test
business.industry
Pattern recognition
CPD
tensor
Nonlinear system
Compressed sensing
recovery
Gaussian noise
symbols
nonlinear
020201 artificial intelligence & image processing
Artificial intelligence
business
030217 neurology & neurosurgery
Subjects
Details
- Language :
- English
- Database :
- OpenAIRE
- Journal :
- SMC
- Accession number :
- edsair.doi.dedup.....8dbff42e47c6ffb3bfc8e0dc3efb84e2