Tensor Decomposition for EEG Signal Retrieval

TL;DR

This paper explores a tensor-based approach using nonlinear CPD to recover temporal EEG signals independently, demonstrating high correlation with original signals despite noise, thus advancing EEG signal reconstruction methods.

Contribution

It introduces a novel application of nonlinear CPD for temporal EEG signal recovery, focusing on independent signal retrieval and noise robustness, which was previously overlooked.

Findings

Achieved over 95% correlation between original and recovered signals

Demonstrated effective EEG signal recovery in noisy conditions

Validated the approach with resting-state EEG data

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 non-negative 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.