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Optimizing mental workload estimation by detecting baseline state using vector phase analysis approach

Improper baseline return from the previous task-evoked hemodynamic response (HR) can contribute to a large variation in the subsequent HR, affecting the estimation of mental workload in brain-computer interface systems. In this study, we proposed a method using vector phase analysis to detect the ba...

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Main Authors: Lim, L.G., Ung, W.C., Chan, Y.L., Lu, C.-K., Funane, T., Kiguchi, M., Tang, T.B.
Format: Article
Published: Institute of Electrical and Electronics Engineers Inc. 2021
Online Access:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85101768113&doi=10.1109%2fTNSRE.2021.3062117&partnerID=40&md5=713cebec3c95897381ad2e2a210dffce
http://eprints.utp.edu.my/23766/
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spelling my.utp.eprints.237662021-08-19T13:10:39Z Optimizing mental workload estimation by detecting baseline state using vector phase analysis approach Lim, L.G. Ung, W.C. Chan, Y.L. Lu, C.-K. Funane, T. Kiguchi, M. Tang, T.B. Improper baseline return from the previous task-evoked hemodynamic response (HR) can contribute to a large variation in the subsequent HR, affecting the estimation of mental workload in brain-computer interface systems. In this study, we proposed a method using vector phase analysis to detect the baseline state as being optimal or suboptimal. We hypothesize that selecting neuronal-related HR as observed in the optimal-baseline blocks can lead to an improvement in estimating mental workload. Oxygenated and deoxygenated hemoglobin concentration changes were integrated as parts of the vector phase. The proposed method was applied to a block-design functional near-infrared spectroscopy dataset (total blocks = 1384), measured on 24 subjects performing multiple difficulty levels of mental arithmetic task. Significant differences in hemodynamic signal change were observed between the optimal- and suboptimal-baseline blocks detected using the proposed method. This supports the effectiveness of the proposed method in detecting baseline state for better estimation of mental workload. The results further highlight the need of customized recovery duration. In short, the proposed method offers a practical approach to detect task-evoked signals, without the need of extra probes. © 2001-2011 IEEE. Institute of Electrical and Electronics Engineers Inc. 2021 Article NonPeerReviewed https://www.scopus.com/inward/record.uri?eid=2-s2.0-85101768113&doi=10.1109%2fTNSRE.2021.3062117&partnerID=40&md5=713cebec3c95897381ad2e2a210dffce Lim, L.G. and Ung, W.C. and Chan, Y.L. and Lu, C.-K. and Funane, T. and Kiguchi, M. and Tang, T.B. (2021) Optimizing mental workload estimation by detecting baseline state using vector phase analysis approach. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 29 . pp. 597-606. http://eprints.utp.edu.my/23766/
institution Universiti Teknologi Petronas
building UTP Resource Centre
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Petronas
content_source UTP Institutional Repository
url_provider http://eprints.utp.edu.my/
description Improper baseline return from the previous task-evoked hemodynamic response (HR) can contribute to a large variation in the subsequent HR, affecting the estimation of mental workload in brain-computer interface systems. In this study, we proposed a method using vector phase analysis to detect the baseline state as being optimal or suboptimal. We hypothesize that selecting neuronal-related HR as observed in the optimal-baseline blocks can lead to an improvement in estimating mental workload. Oxygenated and deoxygenated hemoglobin concentration changes were integrated as parts of the vector phase. The proposed method was applied to a block-design functional near-infrared spectroscopy dataset (total blocks = 1384), measured on 24 subjects performing multiple difficulty levels of mental arithmetic task. Significant differences in hemodynamic signal change were observed between the optimal- and suboptimal-baseline blocks detected using the proposed method. This supports the effectiveness of the proposed method in detecting baseline state for better estimation of mental workload. The results further highlight the need of customized recovery duration. In short, the proposed method offers a practical approach to detect task-evoked signals, without the need of extra probes. © 2001-2011 IEEE.
format Article
author Lim, L.G.
Ung, W.C.
Chan, Y.L.
Lu, C.-K.
Funane, T.
Kiguchi, M.
Tang, T.B.
spellingShingle Lim, L.G.
Ung, W.C.
Chan, Y.L.
Lu, C.-K.
Funane, T.
Kiguchi, M.
Tang, T.B.
Optimizing mental workload estimation by detecting baseline state using vector phase analysis approach
author_facet Lim, L.G.
Ung, W.C.
Chan, Y.L.
Lu, C.-K.
Funane, T.
Kiguchi, M.
Tang, T.B.
author_sort Lim, L.G.
title Optimizing mental workload estimation by detecting baseline state using vector phase analysis approach
title_short Optimizing mental workload estimation by detecting baseline state using vector phase analysis approach
title_full Optimizing mental workload estimation by detecting baseline state using vector phase analysis approach
title_fullStr Optimizing mental workload estimation by detecting baseline state using vector phase analysis approach
title_full_unstemmed Optimizing mental workload estimation by detecting baseline state using vector phase analysis approach
title_sort optimizing mental workload estimation by detecting baseline state using vector phase analysis approach
publisher Institute of Electrical and Electronics Engineers Inc.
publishDate 2021
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-85101768113&doi=10.1109%2fTNSRE.2021.3062117&partnerID=40&md5=713cebec3c95897381ad2e2a210dffce
http://eprints.utp.edu.my/23766/
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score 13.222552

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