  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.
