Functional near-infrared spectroscopy adaptive cognitive training system (FACTS) for cognitive underload and overload prevention: A feasibility study

Ung, W.C. and Meriaudeau, F. and Kiguchi, M. and Tang, T.B. (2020) Functional near-infrared spectroscopy adaptive cognitive training system (FACTS) for cognitive underload and overload prevention: A feasibility study. IEEE Access, 8. pp. 172939-172950. ISSN 21693536

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Abstract

Mismatch between mental workload and working memory capacity can cause mental underload or overload. Adopting the Yerkes-Dodson law as the framework, functional near-infrared spectroscopy adaptive cognitive training system (FACTS) has been developed, whereby the mental workload shall never exceed an individual's capacity, to prevent those unintended conditions. It works by monitoring mental workload in real time and performing dynamic difficulty adjustment accordingly. The feasibility study involved thirty-seven healthy participants undergoing mental arithmetic task with and without FACTS. Without FACTS, the participants not only showed higher NASA Task Load Index scores but also poorer task performance and a significant drop in DLPFC activation towards the end of the task, signifying more severe mental overload. Conversely, they continued to exhibit manifestation of productive learning with FACTS despite showing early signs of mental overload. The study results demonstrated that it is feasible to implement the concept of FACTS. The actual gains from cognitive training will be investigated in future longitudinal study. © This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/

Item Type: Article
Additional Information: cited By 3
Uncontrolled Keywords: Infrared devices; NASA; Near infrared spectroscopy; Planning, Cognitive training; Feasibility studies; Functional near infrared spectroscopy; Longitudinal study; Mental arithmetic; Productive learning; Task performance; Yerkes-dodson laws, Flexible AC transmission systems
Depositing User: Mr Ahmad Suhairi UTP
Date Deposited: 10 Nov 2023 03:28
Last Modified: 10 Nov 2023 03:28
URI: https://khub.utp.edu.my/scholars/id/eprint/13613

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