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EEG/fNIRS Based Workload Classification Using Functional Brain Connectivity and Machine Learning

There is high demand for techniques to estimate human mental workload during some activities for productivity enhancement or accident prevention. Most studies focus on a single physiological sensing modality and use univariate methods to analyse multi-channel electroencephalography (EEG) data. This...

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Autores principales: Cao, Jun, Garro, Enara Martin, Zhao, Yifan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9571712/
https://www.ncbi.nlm.nih.gov/pubmed/36236725
http://dx.doi.org/10.3390/s22197623
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author Cao, Jun
Garro, Enara Martin
Zhao, Yifan
author_facet Cao, Jun
Garro, Enara Martin
Zhao, Yifan
author_sort Cao, Jun
collection PubMed
description There is high demand for techniques to estimate human mental workload during some activities for productivity enhancement or accident prevention. Most studies focus on a single physiological sensing modality and use univariate methods to analyse multi-channel electroencephalography (EEG) data. This paper proposes a new framework that relies on the features of hybrid EEG–functional near-infrared spectroscopy (EEG–fNIRS), supported by machine-learning features to deal with multi-level mental workload classification. Furthermore, instead of the well-used univariate power spectral density (PSD) for EEG recording, we propose using bivariate functional brain connectivity (FBC) features in the time and frequency domains of three bands: delta (0.5–4 Hz), theta (4–7 Hz) and alpha (8–15 Hz). With the assistance of the fNIRS oxyhemoglobin and deoxyhemoglobin (HbO and HbR) indicators, the FBC technique significantly improved classification performance at a 77% accuracy for 0-back vs. 2-back and 83% for 0-back vs. 3-back using a public dataset. Moreover, topographic and heat-map visualisation indicated that the distinguishing regions for EEG and fNIRS showed a difference among the 0-back, 2-back and 3-back test results. It was determined that the best region to assist the discrimination of the mental workload for EEG and fNIRS is different. Specifically, the posterior area performed the best for the posterior midline occipital (POz) EEG in the alpha band and fNIRS had superiority in the right frontal region (AF8).
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spelling pubmed-95717122022-10-17 EEG/fNIRS Based Workload Classification Using Functional Brain Connectivity and Machine Learning Cao, Jun Garro, Enara Martin Zhao, Yifan Sensors (Basel) Article There is high demand for techniques to estimate human mental workload during some activities for productivity enhancement or accident prevention. Most studies focus on a single physiological sensing modality and use univariate methods to analyse multi-channel electroencephalography (EEG) data. This paper proposes a new framework that relies on the features of hybrid EEG–functional near-infrared spectroscopy (EEG–fNIRS), supported by machine-learning features to deal with multi-level mental workload classification. Furthermore, instead of the well-used univariate power spectral density (PSD) for EEG recording, we propose using bivariate functional brain connectivity (FBC) features in the time and frequency domains of three bands: delta (0.5–4 Hz), theta (4–7 Hz) and alpha (8–15 Hz). With the assistance of the fNIRS oxyhemoglobin and deoxyhemoglobin (HbO and HbR) indicators, the FBC technique significantly improved classification performance at a 77% accuracy for 0-back vs. 2-back and 83% for 0-back vs. 3-back using a public dataset. Moreover, topographic and heat-map visualisation indicated that the distinguishing regions for EEG and fNIRS showed a difference among the 0-back, 2-back and 3-back test results. It was determined that the best region to assist the discrimination of the mental workload for EEG and fNIRS is different. Specifically, the posterior area performed the best for the posterior midline occipital (POz) EEG in the alpha band and fNIRS had superiority in the right frontal region (AF8). MDPI 2022-10-08 /pmc/articles/PMC9571712/ /pubmed/36236725 http://dx.doi.org/10.3390/s22197623 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Cao, Jun
Garro, Enara Martin
Zhao, Yifan
EEG/fNIRS Based Workload Classification Using Functional Brain Connectivity and Machine Learning
title EEG/fNIRS Based Workload Classification Using Functional Brain Connectivity and Machine Learning
title_full EEG/fNIRS Based Workload Classification Using Functional Brain Connectivity and Machine Learning
title_fullStr EEG/fNIRS Based Workload Classification Using Functional Brain Connectivity and Machine Learning
title_full_unstemmed EEG/fNIRS Based Workload Classification Using Functional Brain Connectivity and Machine Learning
title_short EEG/fNIRS Based Workload Classification Using Functional Brain Connectivity and Machine Learning
title_sort eeg/fnirs based workload classification using functional brain connectivity and machine learning
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9571712/
https://www.ncbi.nlm.nih.gov/pubmed/36236725
http://dx.doi.org/10.3390/s22197623
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