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Hernández-Sabaté A, Yauri J, Folch P, Álvarez D, Gil D. EEG Dataset Collection for Mental Workload Predictions in Flight-Deck Environment. SENSORS (BASEL, SWITZERLAND) 2024; 24:1174. [PMID: 38400332 PMCID: PMC10891818 DOI: 10.3390/s24041174] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/24/2023] [Revised: 01/26/2024] [Accepted: 02/05/2024] [Indexed: 02/25/2024]
Abstract
High mental workload reduces human performance and the ability to correctly carry out complex tasks. In particular, aircraft pilots enduring high mental workloads are at high risk of failure, even with catastrophic outcomes. Despite progress, there is still a lack of knowledge about the interrelationship between mental workload and brain functionality, and there is still limited data on flight-deck scenarios. Although recent emerging deep-learning (DL) methods using physiological data have presented new ways to find new physiological markers to detect and assess cognitive states, they demand large amounts of properly annotated datasets to achieve good performance. We present a new dataset of electroencephalogram (EEG) recordings specifically collected for the recognition of different levels of mental workload. The data were recorded from three experiments, where participants were induced to different levels of workload through tasks of increasing cognition demand. The first involved playing the N-back test, which combines memory recall with arithmetical skills. The second was playing Heat-the-Chair, a serious game specifically designed to emphasize and monitor subjects under controlled concurrent tasks. The third was flying in an Airbus320 simulator and solving several critical situations. The design of the dataset has been validated on three different levels: (1) correlation of the theoretical difficulty of each scenario to the self-perceived difficulty and performance of subjects; (2) significant difference in EEG temporal patterns across the theoretical difficulties and (3) usefulness for the training and evaluation of AI models.
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Affiliation(s)
- Aura Hernández-Sabaté
- Computer Vision Center (CVC), C/ Sitges, Edifici O, 08193 Bellaterra, Spain; (J.Y.); (D.G.)
- Engineering School, Universitat Autònoma de Barcelona, C/ Sitges, Edifici Q, 08193 Bellaterra, Spain;
| | - José Yauri
- Computer Vision Center (CVC), C/ Sitges, Edifici O, 08193 Bellaterra, Spain; (J.Y.); (D.G.)
| | - Pau Folch
- Engineering School, Universitat Autònoma de Barcelona, C/ Sitges, Edifici Q, 08193 Bellaterra, Spain;
| | | | - Debora Gil
- Computer Vision Center (CVC), C/ Sitges, Edifici O, 08193 Bellaterra, Spain; (J.Y.); (D.G.)
- Engineering School, Universitat Autònoma de Barcelona, C/ Sitges, Edifici Q, 08193 Bellaterra, Spain;
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Page C, Liu CC, Meltzer J, Ghosh Hajra S. Blink-Related Oscillations Provide Naturalistic Assessments of Brain Function and Cognitive Workload within Complex Real-World Multitasking Environments. SENSORS (BASEL, SWITZERLAND) 2024; 24:1082. [PMID: 38400241 PMCID: PMC10892680 DOI: 10.3390/s24041082] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/14/2023] [Revised: 01/14/2024] [Accepted: 01/30/2024] [Indexed: 02/25/2024]
Abstract
BACKGROUND There is a significant need to monitor human cognitive performance in complex environments, with one example being pilot performance. However, existing assessments largely focus on subjective experiences (e.g., questionnaires) and the evaluation of behavior (e.g., aircraft handling) as surrogates for cognition or utilize brainwave measures which require artificial setups (e.g., simultaneous auditory stimuli) that intrude on the primary tasks. Blink-related oscillations (BROs) are a recently discovered neural phenomenon associated with spontaneous blinking that can be captured without artificial setups and are also modulated by cognitive loading and the external sensory environment-making them ideal for brain function assessment within complex operational settings. METHODS Electroencephalography (EEG) data were recorded from eight adult participants (five F, M = 21.1 years) while they completed the Multi-Attribute Task Battery under three different cognitive loading conditions. BRO responses in time and frequency domains were derived from the EEG data, and comparisons of BRO responses across cognitive loading conditions were undertaken. Simultaneously, assessments of blink behavior were also undertaken. RESULTS Blink behavior assessments revealed decreasing blink rate with increasing cognitive load (p < 0.001). Prototypical BRO responses were successfully captured in all participants (p < 0.001). BRO responses reflected differences in task-induced cognitive loading in both time and frequency domains (p < 0.05). Additionally, reduced pre-blink theta band desynchronization with increasing cognitive load was also observed (p < 0.05). CONCLUSION This study confirms the ability of BRO responses to capture cognitive loading effects as well as preparatory pre-blink cognitive processes in anticipation of the upcoming blink during a complex multitasking situation. These successful results suggest that blink-related neural processing could be a potential avenue for cognitive state evaluation in operational settings-both specialized environments such as cockpits, space exploration, military units, etc. and everyday situations such as driving, athletics, human-machine interactions, etc.-where human cognition needs to be seamlessly monitored and optimized.
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Affiliation(s)
- Cleo Page
- Division of Engineering Science, University of Toronto, Toronto, ON M5S 2E4, Canada
| | - Careesa Chang Liu
- Department of Biomedical Engineering and Science, Florida Institute of Technology, 150 W University Boulevard, Melbourne, FL 32901, USA;
| | - Jed Meltzer
- Baycrest Health Sciences, Toronto, ON M6A 2E1, Canada
| | - Sujoy Ghosh Hajra
- Department of Biomedical Engineering and Science, Florida Institute of Technology, 150 W University Boulevard, Melbourne, FL 32901, USA;
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Massaeli F, Bagheri M, Power SD. EEG-based detection of modality-specific visual and auditory sensory processing. J Neural Eng 2023; 20. [PMID: 36749989 DOI: 10.1088/1741-2552/acb9be] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2022] [Accepted: 02/07/2023] [Indexed: 02/09/2023]
Abstract
Objective.A passive brain-computer interface (pBCI) is a system that enhances a human-machine interaction by monitoring the mental state of the user and, based on this implicit information, making appropriate modifications to the interaction. Key to the development of such a system is the ability to reliably detect the mental state of interest via neural signals. Many different mental states have been investigated, including fatigue, attention and various emotions, however one of the most commonly studied states is mental workload, i.e. the amount of attentional resources required to perform a task. The emphasis of mental workload studies to date has been almost exclusively on detecting and predicting the 'level' of cognitive resources required (e.g. high vs. low), but we argue that having information regarding the specific 'type' of resources (e.g. visual or auditory) would allow the pBCI to apply more suitable adaption techniques than would be possible knowing just the overall workload level.Approach.15 participants performed carefully designed visual and auditory tasks while electroencephalography (EEG) data was recorded. The tasks were designed to be as similar as possible to one another except for the type of attentional resources required. The tasks were performed at two different levels of demand. Using traditional machine learning algorithms, we investigated, firstly, if EEG can be used to distinguish between auditory and visual processing tasks and, secondly, what effect level of sensory processing demand has on the ability to distinguish between auditory and visual processing tasks.Main results.The results show that at the high level of demand, the auditory vs. visual processing tasks could be distinguished with an accuracy of 77.1% on average. However, in the low demand condition in this experiment, the tasks were not classified with an accuracy exceeding chance.Significance.These results support the feasibility of developing a pBCI for detecting not only the level, but also the type, of attentional resources being required of the user at a given time. Further research is required to determine if there is a threshold of demand under which the type of sensory processing cannot be detected, but even if that is the case, these results are still promising since it is the high end of demand that is of most concern in safety critical scenarios. Such a BCI could help improve safety in high risk occupations by initiating the most effective and efficient possible adaptation strategies when high workload conditions are detected.
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Affiliation(s)
- Faghihe Massaeli
- Faculty of Engineering and Applied Science, Memorial University of Newfoundland, St. Johns, Canada
| | - Mohammad Bagheri
- Faculty of Engineering and Applied Science, Memorial University of Newfoundland, St. Johns, Canada
| | - Sarah D Power
- Faculty of Engineering and Applied Science, Memorial University of Newfoundland, St. Johns, Canada.,Faculty of Medicine, Memorial University of Newfoundland, St. Johns, Canada
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Mastropietro A, Pirovano I, Marciano A, Porcelli S, Rizzo G. Reliability of Mental Workload Index Assessed by EEG with Different Electrode Configurations and Signal Pre-Processing Pipelines. SENSORS (BASEL, SWITZERLAND) 2023; 23:1367. [PMID: 36772409 PMCID: PMC9920504 DOI: 10.3390/s23031367] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/21/2022] [Revised: 01/18/2023] [Accepted: 01/21/2023] [Indexed: 06/18/2023]
Abstract
BACKGROUND AND OBJECTIVE Mental workload (MWL) is a relevant construct involved in all cognitively demanding activities, and its assessment is an important goal in many research fields. This paper aims at evaluating the reproducibility and sensitivity of MWL assessment from EEG signals considering the effects of different electrode configurations and pre-processing pipelines (PPPs). METHODS Thirteen young healthy adults were enrolled and were asked to perform 45 min of Simon's task to elicit a cognitive demand. EEG data were collected using a 32-channel system with different electrode configurations (fronto-parietal; Fz and Pz; Cz) and analyzed using different PPPs, from the simplest bandpass filtering to the combination of filtering, Artifact Subspace Reconstruction (ASR) and Independent Component Analysis (ICA). The reproducibility of MWL indexes estimation and the sensitivity of their changes were assessed using Intraclass Correlation Coefficient and statistical analysis. RESULTS MWL assessed with different PPPs showed reliability ranging from good to very good in most of the electrode configurations (average consistency > 0.87 and average absolute agreement > 0.92). Larger fronto-parietal electrode configurations, albeit being more affected by the choice of PPPs, provide better sensitivity in the detection of MWL changes if compared to a single-electrode configuration (18 vs. 10 statistically significant differences detected, respectively). CONCLUSIONS The most complex PPPs have been proven to ensure good reliability (>0.90) and sensitivity in all experimental conditions. In conclusion, we propose to use at least a two-electrode configuration (Fz and Pz) and complex PPPs including at least the ICA algorithm (even better including ASR) to mitigate artifacts and obtain reliable and sensitive MWL assessment during cognitive tasks.
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Affiliation(s)
- Alfonso Mastropietro
- Institute of Biomedical Technologies, National Research Council, Via Fratelli Cervi 93, 20054 Segrate, Italy
| | - Ileana Pirovano
- Institute of Biomedical Technologies, National Research Council, Via Fratelli Cervi 93, 20054 Segrate, Italy
| | - Alessio Marciano
- Department of Molecular Medicine, University of Pavia, Via Forlanini 6, 27100 Pavia, Italy
| | - Simone Porcelli
- Department of Molecular Medicine, University of Pavia, Via Forlanini 6, 27100 Pavia, Italy
| | - Giovanna Rizzo
- Institute of Biomedical Technologies, National Research Council, Via Fratelli Cervi 93, 20054 Segrate, Italy
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Chen L, Li H, Zhao L, Tian F, Tian S, Shao J. The effect of job satisfaction regulating workload on miners' unsafe state. Sci Rep 2022; 12:16375. [PMID: 36180557 PMCID: PMC9525713 DOI: 10.1038/s41598-022-20673-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2022] [Accepted: 09/16/2022] [Indexed: 11/09/2022] Open
Abstract
Miners’ unsafe behavior is the main cause of accidents in coal mines, and unsafe state have an important influence on unsafe behavior among miners. To minimize accidents from the source of accident chain, we evaluated the impact of workload on miners’ unsafe state. It is important for coal enterprises to monitor miners’ unsafe state and to prevent unsafe accidents. Workload is divided into two dimensions: work time and work demand. Meanwhile, we introduced job satisfaction as a moderating variable. Through empirical research methods, first-line employees from two coal mines in China were enrolled in the questionnaire survey. Regression analysis was used to verify the impact of workload and its various dimensions, job satisfaction, and miners’ unsafe state. We found that workload, work time and work demand have significant positive effects on miners’ unsafe state. Job satisfaction plays a moderating effect in the relationship between workload and miners’ unsafe state. To some extent, a higher job satisfaction was associated with reduced workload, reduced occurrence of miners’ unsafe state and minimal incidences of unsafe accidents. On this basis, measures were proposed to improve miners’ unsafe state in terms of workload and job satisfaction. This study informs the establishment of effective intervention measures to monitor miners’ unsafe state and is also beneficial to the improvement of coal mine safety.
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Affiliation(s)
- Lei Chen
- College of Safety Science and Engineering, Xi'an University of Science and Technology, Xi'an, 710054, China. .,Institute of Safety and Emergency Management, Xi'an University of Science and Technology, Xi'an, 710054, China. .,School of Management, Henan Institute of Urban Construction, Pingdingshan, 467000, China.
| | - Hongxia Li
- College of Safety Science and Engineering, Xi'an University of Science and Technology, Xi'an, 710054, China. .,Institute of Safety and Emergency Management, Xi'an University of Science and Technology, Xi'an, 710054, China. .,School of Management, Xi'an University of Science and Technology, Xi'an, 710054, China.
| | - Lin Zhao
- College of Safety Science and Engineering, Xi'an University of Science and Technology, Xi'an, 710054, China.,Institute of Safety and Emergency Management, Xi'an University of Science and Technology, Xi'an, 710054, China
| | - Fangyuan Tian
- Institute of Safety and Emergency Management, Xi'an University of Science and Technology, Xi'an, 710054, China.,School of Management, Xi'an University of Science and Technology, Xi'an, 710054, China
| | - Shuicheng Tian
- College of Safety Science and Engineering, Xi'an University of Science and Technology, Xi'an, 710054, China.,Institute of Safety and Emergency Management, Xi'an University of Science and Technology, Xi'an, 710054, China
| | - Jiang Shao
- School of Architecture & Design, China University of Mining and Technology, Xuzhou, 221116, China
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Aygun A, Nguyen T, Haga Z, Aeron S, Scheutz M. Investigating Methods for Cognitive Workload Estimation for Assistive Robots. SENSORS (BASEL, SWITZERLAND) 2022; 22:6834. [PMID: 36146189 PMCID: PMC9505485 DOI: 10.3390/s22186834] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/28/2022] [Revised: 08/29/2022] [Accepted: 09/05/2022] [Indexed: 06/16/2023]
Abstract
Robots interacting with humans in assistive contexts have to be sensitive to human cognitive states to be able to provide help when it is needed and not overburden the human when the human is busy. Yet, it is currently still unclear which sensing modality might allow robots to derive the best evidence of human workload. In this work, we analyzed and modeled data from a multi-modal simulated driving study specifically designed to evaluate different levels of cognitive workload induced by various secondary tasks such as dialogue interactions and braking events in addition to the primary driving task. Specifically, we performed statistical analyses of various physiological signals including eye gaze, electroencephalography, and arterial blood pressure from the healthy volunteers and utilized several machine learning methodologies including k-nearest neighbor, naive Bayes, random forest, support-vector machines, and neural network-based models to infer human cognitive workload levels. Our analyses provide evidence for eye gaze being the best physiological indicator of human cognitive workload, even when multiple signals are combined. Specifically, the highest accuracy (in %) of binary workload classification based on eye gaze signals is 80.45 ∓ 3.15 achieved by using support-vector machines, while the highest accuracy combining eye gaze and electroencephalography is only 77.08 ∓ 3.22 achieved by a neural network-based model. Our findings are important for future efforts of real-time workload estimation in the multimodal human-robot interactive systems given that eye gaze is easy to collect and process and less susceptible to noise artifacts compared to other physiological signal modalities.
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Affiliation(s)
- Ayca Aygun
- Department of Computer Science, Tufts University, Medford, MA 02155, USA
| | - Thuan Nguyen
- Department of Computer Science, Tufts University, Medford, MA 02155, USA
| | - Zachary Haga
- Department of Computer Science, Tufts University, Medford, MA 02155, USA
| | - Shuchin Aeron
- Department of Electrical and Computer Engineering, Tufts University, Medford, MA 02155, USA
| | - Matthias Scheutz
- Department of Computer Science, Tufts University, Medford, MA 02155, USA
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Sensitive Channel Selection for Mental Workload Classification. MATHEMATICS 2022. [DOI: 10.3390/math10132266] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/10/2022]
Abstract
Mental workload (MW) assessment has been widely studied in various human–machine interaction tasks. The existing researches on MW classification mostly use non-invasive electroencephalography (EEG) caps to collect EEG signals and identify MW levels. However, the activation region of the brain stimulated by MW tasks is not the same for every subject. It may be inappropriate to use EEG signals from all electrode channels to identify MW. In this paper, an EEG rhythm energy heatmap is first established to visually show the change trends in the energy of four EEG rhythms with time, EEG channels and MW levels. It can be concluded from the presented heatmaps that this change trend varies with subjects, rhythms and channels. Based on the analysis, a double threshold method is proposed to select sensitive channels for MW assessment. The EEG signals of personalized selected channels, named positive sensitive channels (PSCs) and negative sensitive channels (NSCs), are used for MW classification using the Support Vector Machine (SVM) algorithm. The results show that the selection of personalized sensitive channels generally contributes to improving the performance of MW classification.
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Longo L, Wickens CD, Hancock PA, Hancock GM. Human Mental Workload: A Survey and a Novel Inclusive Definition. Front Psychol 2022; 13:883321. [PMID: 35719509 PMCID: PMC9201728 DOI: 10.3389/fpsyg.2022.883321] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2022] [Accepted: 05/10/2022] [Indexed: 12/05/2022] Open
Abstract
Human mental workload is arguably the most invoked multidimensional construct in Human Factors and Ergonomics, getting momentum also in Neuroscience and Neuroergonomics. Uncertainties exist in its characterization, motivating the design and development of computational models, thus recently and actively receiving support from the discipline of Computer Science. However, its role in human performance prediction is assured. This work is aimed at providing a synthesis of the current state of the art in human mental workload assessment through considerations, definitions, measurement techniques as well as applications, Findings suggest that, despite an increasing number of associated research works, a single, reliable and generally applicable framework for mental workload research does not yet appear fully established. One reason for this gap is the existence of a wide swath of operational definitions, built upon different theoretical assumptions which are rarely examined collectively. A second reason is that the three main classes of measures, which are self-report, task performance, and physiological indices, have been used in isolation or in pairs, but more rarely in conjunction all together. Multiple definitions complement each another and we propose a novel inclusive definition of mental workload to support the next generation of empirical-based research. Similarly, by comprehensively employing physiological, task-performance, and self-report measures, more robust assessments of mental workload can be achieved.
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Affiliation(s)
- Luca Longo
- Artificial Intelligence and Cognitive Load Lab, The Applied Intelligence Research Centre, School of Computer Science, Technological University Dublin, Dublin, Ireland
| | - Christoper D Wickens
- Department of Psychology, Colorado State University, Fort Collins, CO, United States
| | - Peter A Hancock
- Department of Psychology, Institute for Simulation and Training, University of Central Florida, Orlando, FL, United States
| | - Gabriela M Hancock
- Department of Psychology, California State University, Long Beach, CA, United States
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Mental Workload Classification Method Based on EEG Cross-Session Subspace Alignment. MATHEMATICS 2022. [DOI: 10.3390/math10111875] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Electroencephalogram (EEG) signals are sensitive to the level of Mental Workload (MW). However, the random non-stationarity of EEG signals will lead to low accuracy and a poor generalization ability for cross-session MW classification. To solve this problem of the different marginal distribution of EEG signals in different time periods, an MW classification method based on EEG Cross-Session Subspace Alignment (CSSA) is presented to identify the level of MW induced in visual manipulation tasks. The Independent Component Analysis (ICA) method is used to obtain the Independent Components (ICs) of labeled and unlabeled EEG signals. The energy features of ICs are extracted as source domains and target domains, respectively. The marginal distributions of source subspace base vectors are aligned with the target subspace base vectors based on the linear mapping. The Kullback–Leibler (KL) divergences between the two domains are calculated to select approximately similar transformed base vectors of source subspace. The energy features in all selected vectors are trained to build a new classifier using the Support Vector Machine (SVM). Then it can realize MW classification using the cross-session EEG signals, and has good classification accuracy.
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Chu H, Cao Y, Jiang J, Yang J, Huang M, Li Q, Jiang C, Jiao X. Optimized electroencephalogram and functional near-infrared spectroscopy-based mental workload detection method for practical applications. Biomed Eng Online 2022; 21:9. [PMID: 35109879 PMCID: PMC8812267 DOI: 10.1186/s12938-022-00980-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2021] [Accepted: 01/21/2022] [Indexed: 11/14/2022] Open
Abstract
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 O2Hb 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 \documentclass[12pt]{minimal}
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\begin{document}$$\beta_{2}$$\end{document}β2 bands in the occipital region increased significantly with task difficulty. The changing range and the mean amplitude of O2Hb 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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Affiliation(s)
- Hongzuo Chu
- National Key Laboratory of Human Factors Engineering, China Astronaut Research and Training Center, Beijing, China.,Space Engineering University, Beijing, China
| | - Yong Cao
- National Key Laboratory of Human Factors Engineering, China Astronaut Research and Training Center, Beijing, China
| | - Jin Jiang
- National Key Laboratory of Human Factors Engineering, China Astronaut Research and Training Center, Beijing, China
| | - Jiehong Yang
- National Key Laboratory of Human Factors Engineering, China Astronaut Research and Training Center, Beijing, China.,Space Engineering University, Beijing, China
| | - Mengyin Huang
- National Key Laboratory of Human Factors Engineering, China Astronaut Research and Training Center, Beijing, China.,Space Engineering University, Beijing, China
| | - Qijie Li
- National Key Laboratory of Human Factors Engineering, China Astronaut Research and Training Center, Beijing, China
| | - Changhua Jiang
- National Key Laboratory of Human Factors Engineering, China Astronaut Research and Training Center, Beijing, China.
| | - Xuejun Jiao
- National Key Laboratory of Human Factors Engineering, China Astronaut Research and Training Center, Beijing, China. .,Space Engineering University, Beijing, China.
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Sweeti. Attentional load classification in multiple object tracking task using optimized support vector machine classifier: a step towards cognitive brain-computer interface. J Med Eng Technol 2021; 46:69-77. [PMID: 34825850 DOI: 10.1080/03091902.2021.1992519] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]
Abstract
Cognitive brain-computer interface (cBCI) is an emerging area with applications in neurorehabilitation and performance monitoring. cBCI works on the cognitive brain signal that does not require a person to pay much effort unlike the motor brain-computer interface (BCI) however existing cBCI systems currently offer lower accuracy than the motor BCI. Since attention is one of the cognitive signals that can be used to realise the cBCI, this work uses the multiple object tracking (MOT) task to acquire the desired electroencephalograph (EEG) signal from healthy subjects. The main objective of the paper is to explore the preliminary applications of support vector machine (SVM) classifier to classify the attentional load in multiple object tracking task. Results show that the attentional load can be classified using SVM with sensitivity, specificity, and accuracy of 94.03%, 92.50%, and 93.28%, respectively using the spectral entropy EEG feature. The classification performance promises the potential application of the current approach in the cognitive brain-computer interface for neurorehabilitation.
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Affiliation(s)
- Sweeti
- Medical Electronics Engineering Department, M. S. Ramaiah Institute of Technology, Bangalore, India
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12
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Subject-specific mental workload classification using EEG and stochastic configuration network (SCN). Biomed Signal Process Control 2021. [DOI: 10.1016/j.bspc.2021.102711] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
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13
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Qu H, Gao X, Pang L. Classification of mental workload based on multiple features of ECG signals. INFORMATICS IN MEDICINE UNLOCKED 2021. [DOI: 10.1016/j.imu.2021.100575] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022] Open
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