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Optimized electroencephalogram and functional near-infrared spectroscopy-based mental workload detection method for practical applications

BACKGROUND: Mental workload is a critical consideration in complex man–machine systems design. Among various mental workload detection techniques, multimodal detection techniques integrating electroencephalogram (EEG) and functional near-infrared spectroscopy (fNIRS) signals have attracted considera...

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Autores principales: Chu, Hongzuo, Cao, Yong, Jiang, Jin, Yang, Jiehong, Huang, Mengyin, Li, Qijie, Jiang, Changhua, Jiao, Xuejun
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8812267/
https://www.ncbi.nlm.nih.gov/pubmed/35109879
http://dx.doi.org/10.1186/s12938-022-00980-1
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author Chu, Hongzuo
Cao, Yong
Jiang, Jin
Yang, Jiehong
Huang, Mengyin
Li, Qijie
Jiang, Changhua
Jiao, Xuejun
author_facet Chu, Hongzuo
Cao, Yong
Jiang, Jin
Yang, Jiehong
Huang, Mengyin
Li, Qijie
Jiang, Changhua
Jiao, Xuejun
author_sort Chu, Hongzuo
collection PubMed
description BACKGROUND: Mental workload is a critical consideration in complex man–machine systems design. Among various mental workload detection techniques, multimodal detection techniques integrating electroencephalogram (EEG) and functional near-infrared spectroscopy (fNIRS) signals have attracted considerable attention. However, existing EEG–fNIRS-based mental workload detection methods have certain defects, such as complex signal acquisition channels and low detection accuracy, which restrict their practical application. METHODS: The signal acquisition configuration was optimized by analyzing the feature importance in mental workload recognition model and a more accurate and convenient EEG–fNIRS-based mental workload detection method was constructed. A classical Multi-Task Attribute Battery (MATB) task was conducted with 20 participating volunteers. Subjective scale data, 64-channel EEG data, and two-channel fNIRS data were collected. RESULTS: A higher number of EEG channels correspond to higher detection accuracy. However, there is no obvious improvement in accuracy once the number of EEG channels reaches 26, with a four-level mental workload detection accuracy of 76.25 ± 5.21%. Partial results of physiological analysis verify the results of previous studies, such as that the θ power of EEG and concentration of O(2)Hb in the prefrontal region increase while the concentration of HHb decreases with task difficulty. It was further observed, for the first time, that the energy of each band of EEG signals was significantly different in the occipital lobe region, and the power of [Formula: see text] and [Formula: see text] bands in the occipital region increased significantly with task difficulty. The changing range and the mean amplitude of O(2)Hb in high-difficulty tasks were significantly higher compared with those in low-difficulty tasks. CONCLUSIONS: The channel configuration of EEG–fNIRS-based mental workload detection was optimized to 26 EEG channels and two frontal fNIRS channels. A four-level mental workload detection accuracy of 76.25 ± 5.21% was obtained, which is higher than previously reported results. The proposed configuration can promote the application of mental workload detection technology in military, driving, and other complex human–computer interaction systems.
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spelling pubmed-88122672022-02-07 Optimized electroencephalogram and functional near-infrared spectroscopy-based mental workload detection method for practical applications Chu, Hongzuo Cao, Yong Jiang, Jin Yang, Jiehong Huang, Mengyin Li, Qijie Jiang, Changhua Jiao, Xuejun Biomed Eng Online Research BACKGROUND: Mental workload is a critical consideration in complex man–machine systems design. Among various mental workload detection techniques, multimodal detection techniques integrating electroencephalogram (EEG) and functional near-infrared spectroscopy (fNIRS) signals have attracted considerable attention. However, existing EEG–fNIRS-based mental workload detection methods have certain defects, such as complex signal acquisition channels and low detection accuracy, which restrict their practical application. METHODS: The signal acquisition configuration was optimized by analyzing the feature importance in mental workload recognition model and a more accurate and convenient EEG–fNIRS-based mental workload detection method was constructed. A classical Multi-Task Attribute Battery (MATB) task was conducted with 20 participating volunteers. Subjective scale data, 64-channel EEG data, and two-channel fNIRS data were collected. RESULTS: A higher number of EEG channels correspond to higher detection accuracy. However, there is no obvious improvement in accuracy once the number of EEG channels reaches 26, with a four-level mental workload detection accuracy of 76.25 ± 5.21%. Partial results of physiological analysis verify the results of previous studies, such as that the θ power of EEG and concentration of O(2)Hb in the prefrontal region increase while the concentration of HHb decreases with task difficulty. It was further observed, for the first time, that the energy of each band of EEG signals was significantly different in the occipital lobe region, and the power of [Formula: see text] and [Formula: see text] bands in the occipital region increased significantly with task difficulty. The changing range and the mean amplitude of O(2)Hb in high-difficulty tasks were significantly higher compared with those in low-difficulty tasks. CONCLUSIONS: The channel configuration of EEG–fNIRS-based mental workload detection was optimized to 26 EEG channels and two frontal fNIRS channels. A four-level mental workload detection accuracy of 76.25 ± 5.21% was obtained, which is higher than previously reported results. The proposed configuration can promote the application of mental workload detection technology in military, driving, and other complex human–computer interaction systems. BioMed Central 2022-02-02 /pmc/articles/PMC8812267/ /pubmed/35109879 http://dx.doi.org/10.1186/s12938-022-00980-1 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Chu, Hongzuo
Cao, Yong
Jiang, Jin
Yang, Jiehong
Huang, Mengyin
Li, Qijie
Jiang, Changhua
Jiao, Xuejun
Optimized electroencephalogram and functional near-infrared spectroscopy-based mental workload detection method for practical applications
title Optimized electroencephalogram and functional near-infrared spectroscopy-based mental workload detection method for practical applications
title_full Optimized electroencephalogram and functional near-infrared spectroscopy-based mental workload detection method for practical applications
title_fullStr Optimized electroencephalogram and functional near-infrared spectroscopy-based mental workload detection method for practical applications
title_full_unstemmed Optimized electroencephalogram and functional near-infrared spectroscopy-based mental workload detection method for practical applications
title_short Optimized electroencephalogram and functional near-infrared spectroscopy-based mental workload detection method for practical applications
title_sort optimized electroencephalogram and functional near-infrared spectroscopy-based mental workload detection method for practical applications
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8812267/
https://www.ncbi.nlm.nih.gov/pubmed/35109879
http://dx.doi.org/10.1186/s12938-022-00980-1
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