eprintid: 7896
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/00/78/96
datestamp: 2023-11-09 16:19:44
lastmod: 2023-11-09 16:19:44
status_changed: 2023-11-09 16:10:41
type: article
metadata_visibility: show
creators_name: Yanti, D.K.
creators_name: Yusoff, M.Z.
creators_name: Asirvadam, V.S.
title: Single-trial visual evoked potential extraction using partial least-squares-based approach
ispublished: pub
keywords: Eigenvalues and eigenfunctions; Electroencephalography; Extraction; Hospitals; Regression analysis, Electroencephalograph (EEG); Latent component; Partial least square (PLS); Single trial; Visual evoked potential, Least squares approximations, 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
note: cited By 4
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.
date: 2016
publisher: Institute of Electrical and Electronics Engineers Inc.
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-84971644004&doi=10.1109%2fJBHI.2014.2367152&partnerID=40&md5=0dee483a5f785d9483c7a07e3c50486c
id_number: 10.1109/JBHI.2014.2367152
full_text_status: none
publication: IEEE Journal of Biomedical and Health Informatics
volume: 20
number: 1
pagerange: 82-90
refereed: TRUE
issn: 21682194
citation:   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.  ISSN 21682194