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Louis LEL, Moussaoui S, Van Langhenhove A, Ravoux S, Le Jan T, Roualdes V, Milleville-Pennel I. Cognitive tasks and combined statistical methods to evaluate, model, and predict mental workload. Front Psychol 2023; 14:1122793. [PMID: 37251030 PMCID: PMC10213687 DOI: 10.3389/fpsyg.2023.1122793] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2023] [Accepted: 04/17/2023] [Indexed: 05/31/2023] Open
Abstract
Mental workload (MWL) is a concept that is used as a reference for assessing the mental cost of activities. In recent times, challenges related to user experience are determining the expected MWL value for a given activity and real-time adaptation of task complexity level to achieve or maintain desired MWL. As a consequence, it is important to have at least one task that can reliably predict the MWL level associated with a given complexity level. In this study, we used several cognitive tasks to meet this need, including the N-Back task, the commonly used reference test in the MWL literature, and the Corsi test. Tasks were adapted to generate different MWL classes measured via NASA-TLX and Workload Profile questionnaires. Our first objective was to identify which tasks had the most distinct MWL classes based on combined statistical methods. Our results indicated that the Corsi test satisfied our first objective, obtaining three distinct MWL classes associated with three complexity levels offering therefore a reliable model (about 80% accuracy) to predicted MWL classes. Our second objective was to achieve or maintain the desired MWL, which entailed the use of an algorithm to adapt the MWL class based on an accurate prediction model. This model needed to be based on an objective and real-time indicator of MWL. For this purpose, we identified different performance criteria for each task. The classification models obtained indicated that only the Corsi test would be a good candidate for this aim (more than 50% accuracy compared to a chance level of 33%) but performances were not sufficient to consider identifying and adapting the MWL class online with sufficient accuracy during a task. Thus, performance indicators require to be complemented by other types of measures like physiological ones. Our study also highlights the limitations of the N-back task in favor of the Corsi test which turned out to be the best candidate to model and predict the MWL among several cognitive tasks.
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Affiliation(s)
- Lina-Estelle Linelle Louis
- Entreprise Onepoint, Nantes, France
- École Centrale Nantes, CNRS, LS2N, UMR 6004, Nantes Université, Nantes, France
| | - Saïd Moussaoui
- École Centrale Nantes, CNRS, LS2N, UMR 6004, Nantes Université, Nantes, France
| | - Aurélien Van Langhenhove
- Department of Neurosurgery, CHU (Centre Hospitalier et Universitaire) Nord Laënnec, Saint-Herblain, France
| | | | - Thomas Le Jan
- Entreprise Onepoint, Nantes, France
- École Centrale Nantes, CNRS, LS2N, UMR 6004, Nantes Université, Nantes, France
| | - Vincent Roualdes
- Department of Neurosurgery, CHU (Centre Hospitalier et Universitaire) Nord Laënnec, Saint-Herblain, France
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Zeng H, Fang X, Zhao Y, Wu J, Li M, Zheng H, Xu F, Pan D, Dai G. EMCI: A Novel EEG-Based Mental Workload Assessment Index of Mild Cognitive Impairment. IEEE TRANSACTIONS ON BIOMEDICAL CIRCUITS AND SYSTEMS 2022; 16:902-914. [PMID: 35951572 DOI: 10.1109/tbcas.2022.3198265] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
As aging deepens, early detection of mild cognitive impairment (MCI) is increasingly important to prevent Alzheimer Dementia (AD) and improve the quality of life of older adults. In recent years, a large number of studies focus on the abnormal brain cognitive function of MCI, while ignoring the quantitative evaluation of MCI's mental workload. In this study, we propose a workload index for MCI screening, named EMCI, which is a linear discriminant cumulative estimate of subjects' electroencephalography (EEG) power spectra in α and β rhythms. Then, we design a matched prototype system to verify the effectiveness of EMCI. The results show that the EMCI is sensitive to changes of subjects' mental workload, and is significantly lower in MCI than in HC (Health control), which may be precisely caused by cognitive dysfunction. The proposed EMCI index can be used for online assessment of mental workload in older adults, which can help achieve quick screening of MCI and provide a critical window for clinical treatment interventions.
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