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Single-trial visual evoked potential extraction using partial least-squares-based approach

Yanti, D.K. and Yusoff, M.Z. and Asirvadam, V.S. (2016) Single-trial visual evoked potential extraction using partial least-squares-based approach. IEEE Journal of Biomedical and Health Informatics, 20 (1). pp. 82-90.

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Official URL: https://www.scopus.com/inward/record.uri?eid=2-s2....

Abstract

A single-trial extraction of a visual evoked potential (VEP) signal based on the partial least-squares (PLS) regression method has been proposed in this paper. This paper has focused on the extraction and estimation of the latencies of P100, P200, P300, N75, and N135 in the artificial electroencephalograph (EEG) signal. The real EEG signal obtained from the hospital was only concentrated on the P100. The performance of the PLS has been evaluated mainly on the basis of latency error rate of the peaks for the artificialEEGsignal, and themean peak detection and standard deviation for the real EEG signal. The simulation results show that the proposed PLS algorithm is capable of reconstructing the EEG signal into its desired shape of the ideal VEP. For P100, the proposed PLS algorithm is able to provide comparable results to the generalized eigenvalue decomposition (GEVD) algorithm, which alters (prewhitens) the EEG input signal using the prestimulation EEG signal. It has also shown better performance for laer peaks (P200 and P300). The PLS outperformed not only in positive peaks but also in N75. In P100, the PLS was comparable with the GEVD although N135 was better estimated by GEVD. The proposed PLS algorithm is comparable to GEVD given that PLS does not alter the EEG input signal. The PLS algorithm gives the best estimate to multitrial ensemble averaging. This research offers benefits such as avoiding patient's fatigue during VEP test measurement in the hospital, in BCI applications and in EEG-fMRI integration. © 2014 IEEE.

Item Type:Article
Impact Factor:cited By 3
Uncontrolled Keywords:Algorithms; Eigenvalues and eigenfunctions; Extraction; Hospitals; Least squares approximations; Regression analysis, Electroencephalograph (EEG); Latent component; Partial least square (PLS); Single trial; Visual evoked potential, Electroencephalography, algorithm; computer simulation; electroencephalography; human; least square analysis; physiology; procedures; signal processing; visual evoked potential, Algorithms; Computer Simulation; Electroencephalography; Evoked Potentials, Visual; Humans; Least-Squares Analysis; Signal Processing, Computer-Assisted
ID Code:25735
Deposited By: Ms Sharifah Fahimah Saiyed Yeop
Deposited On:27 Aug 2021 13:05
Last Modified:27 Aug 2021 13:05

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