Hemodynamic Analysis for Cognitive Load Assessment and Classification in Motor Learning Tasks Using Type-2 Fuzzy Sets

Ghosh, Lidia and Konar, Amit and Rakshit, Pratyusha and Nagar, Atulya K. (2018) Hemodynamic Analysis for Cognitive Load Assessment and Classification in Motor Learning Tasks Using Type-2 Fuzzy Sets. IEEE Transactions on Emerging Topics in Computational Intelligence, 3 (1). pp. 1-16. ISSN 2471-285X (Accepted for Publication)

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Official URL: https://ieeexplore.ieee.org/document/8467365/

Abstract

The paper addresses a novel approach to assess and classify the cognitive load of subjects from their hemodynamic response while engaged in motor learning tasks, such as vehicle-driving. A set of complex motor-activity-learning stimuli for braking, steering-control and acceleration is prepared to experimentally measure and classify the cognitive load of the car-drivers in three distinct classes: High, Medium and Low. New models of General and Interval Type-2 Fuzzy classifiers are proposed to reduce the scope of uncertainty in cognitive load classification due to the fluctuation of the hemodynamic features within and across sessions. The proposed classifiers offer high classification accuracy over 96%, leaving behind the traditional type-1/type-2 fuzzy and other standard classifiers. Experiments undertaken also offer a deep biological insight concerning the shift of brain-activations from the orbito-frontal to the ventro-lateral prefrontal cortex during high-to-low transition in cognitive load. Further, the activation of the dorsolateral prefrontal cortex is also reduced during low cognitive load of subjects. The proposed research outcome may directly be utilized to identify driving learners with low cognitive load for difficult motor learning tasks, such as taking a U-turn in a narrow space and motion control on the top of a bridge to avoid possible collision with the car ahead.

Item Type: Article
Additional Information and Comments: (c) 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.
Keywords: fNIRs, Motor learning, Hemodynamic Response, Cognitive load classification, Type-2 Fuzzy classifiers.
Faculty / Department: Faculty of Science > Mathematics and Computer Science
Depositing User: Atulya Nagar
Date Deposited: 15 Oct 2018 10:40
Last Modified: 15 Oct 2018 10:40
URI: http://hira.hope.ac.uk/id/eprint/2619

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