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

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 electroencepha...

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Main Authors: Yanti, D.K., Yusoff, M.Z., Asirvadam, V.S.
Format: Article
Institution: Universiti Teknologi Petronas
Record Id / ISBN-0: utp-eprints.25509 /
Published: Institute of Electrical and Electronics Engineers Inc. 2016
Online Access: https://www.scopus.com/inward/record.uri?eid=2-s2.0-84971644004&doi=10.1109%2fJBHI.2014.2367152&partnerID=40&md5=0dee483a5f785d9483c7a07e3c50486c
http://eprints.utp.edu.my/25509/
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spelling utp-eprints.255092021-08-27T13:03:24Z Single-trial visual evoked potential extraction using partial least-squares-based approach Yanti, D.K. Yusoff, M.Z. Asirvadam, V.S. 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. Institute of Electrical and Electronics Engineers Inc. 2016 Article NonPeerReviewed https://www.scopus.com/inward/record.uri?eid=2-s2.0-84971644004&doi=10.1109%2fJBHI.2014.2367152&partnerID=40&md5=0dee483a5f785d9483c7a07e3c50486c 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. http://eprints.utp.edu.my/25509/
institution Universiti Teknologi Petronas
collection UTP Institutional Repository
description 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.
format Article
author Yanti, D.K.
Yusoff, M.Z.
Asirvadam, V.S.
spellingShingle Yanti, D.K.
Yusoff, M.Z.
Asirvadam, V.S.
Single-trial visual evoked potential extraction using partial least-squares-based approach
author_sort Yanti, D.K.
title Single-trial visual evoked potential extraction using partial least-squares-based approach
title_short Single-trial visual evoked potential extraction using partial least-squares-based approach
title_full Single-trial visual evoked potential extraction using partial least-squares-based approach
title_fullStr Single-trial visual evoked potential extraction using partial least-squares-based approach
title_full_unstemmed Single-trial visual evoked potential extraction using partial least-squares-based approach
title_sort single-trial visual evoked potential extraction using partial least-squares-based approach
publisher Institute of Electrical and Electronics Engineers Inc.
publishDate 2016
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-84971644004&doi=10.1109%2fJBHI.2014.2367152&partnerID=40&md5=0dee483a5f785d9483c7a07e3c50486c
http://eprints.utp.edu.my/25509/
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score 11.62408

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