Staff View: Application of artificial intelligence in cognitive load analysis using functional near-infrared spectroscopy: A systematic review

Application of artificial intelligence in cognitive load analysis using functional near-infrared spectroscopy: A systematic review

Cognitive load theory suggests that overloading of working memory may negatively affect the performance of human in cognitively demanding tasks. Evaluation of cognitive load is a difficult task; it is often assessed through feedback and evaluation from experts. Cognitive load classification based on...

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Main Authors:	Khan, Mehshan Ahmed, Asadi, Houshyar, Zhang, Li, Qazani, Mohammad Reza Chalak, Oladazimi, Sam, Loo, Chu Kiong, Lim, Chee Peng, Nahavandi, Saeid
Format:	Article
Published:	Elsevier 2024
Subjects:	QA75 Electronic computers. Computer science
Online Access:	http://eprints.um.edu.my/45399/ https://doi.org/10.1016/j.eswa.2024.123717
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spelling	my.um.eprints.453992024-10-16T07:26:35Z http://eprints.um.edu.my/45399/ Application of artificial intelligence in cognitive load analysis using functional near-infrared spectroscopy: A systematic review Khan, Mehshan Ahmed Asadi, Houshyar Zhang, Li Qazani, Mohammad Reza Chalak Oladazimi, Sam Loo, Chu Kiong Lim, Chee Peng Nahavandi, Saeid QA75 Electronic computers. Computer science Cognitive load theory suggests that overloading of working memory may negatively affect the performance of human in cognitively demanding tasks. Evaluation of cognitive load is a difficult task; it is often assessed through feedback and evaluation from experts. Cognitive load classification based on Functional Near-InfraRed Spectroscopy (fNIRS) is now one of the key research areas in recent years, due to its resistance of artefacts, costeffectiveness, and portability. To make fNIRS more practical in various applications, it is necessary to develop robust algorithms that can automatically classify fNIRS signals and less reliant on trained signals. Many of the analytical tools used in cognitive sciences have used Deep Learning (DL) modalities to uncover relevant information for mental workload classification. This review investigates the research questions on the design and overall effectiveness of DL as well as its key characteristics. We have identified 45 studies published between 2011 and 2023, that specifically proposed Machine Learning (ML) models for classifying cognitive load using data obtained from fNIRS devices. Those studies were analyzed based on type of feature selection methods, input, and DL model architectures. Most of the existing cognitive load studies are based on ML algorithms, which follow signal filtration and hand-crafted features. It is observed that hybrid DL architectures that integrate convolution and LSTM operators performed significantly better in comparison with other models. However, DL models especially hybrid models have not been extensively investigated for the classification of cognitive load captured by fNIRS devices. The current trends and challenges are highlighted to provide directions for the development of DL models pertaining to fNIRS research. Elsevier 2024-09 Article PeerReviewed Khan, Mehshan Ahmed and Asadi, Houshyar and Zhang, Li and Qazani, Mohammad Reza Chalak and Oladazimi, Sam and Loo, Chu Kiong and Lim, Chee Peng and Nahavandi, Saeid (2024) Application of artificial intelligence in cognitive load analysis using functional near-infrared spectroscopy: A systematic review. Expert Systems with Applications, 249 (C). p. 123717. ISSN 0957-4174, DOI https://doi.org/10.1016/j.eswa.2024.123717 <https://doi.org/10.1016/j.eswa.2024.123717>. https://doi.org/10.1016/j.eswa.2024.123717 10.1016/j.eswa.2024.123717
institution	Universiti Malaya
building	UM Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Malaya
content_source	UM Research Repository
url_provider	http://eprints.um.edu.my/
topic	QA75 Electronic computers. Computer science
spellingShingle	QA75 Electronic computers. Computer science Khan, Mehshan Ahmed Asadi, Houshyar Zhang, Li Qazani, Mohammad Reza Chalak Oladazimi, Sam Loo, Chu Kiong Lim, Chee Peng Nahavandi, Saeid Application of artificial intelligence in cognitive load analysis using functional near-infrared spectroscopy: A systematic review
description	Cognitive load theory suggests that overloading of working memory may negatively affect the performance of human in cognitively demanding tasks. Evaluation of cognitive load is a difficult task; it is often assessed through feedback and evaluation from experts. Cognitive load classification based on Functional Near-InfraRed Spectroscopy (fNIRS) is now one of the key research areas in recent years, due to its resistance of artefacts, costeffectiveness, and portability. To make fNIRS more practical in various applications, it is necessary to develop robust algorithms that can automatically classify fNIRS signals and less reliant on trained signals. Many of the analytical tools used in cognitive sciences have used Deep Learning (DL) modalities to uncover relevant information for mental workload classification. This review investigates the research questions on the design and overall effectiveness of DL as well as its key characteristics. We have identified 45 studies published between 2011 and 2023, that specifically proposed Machine Learning (ML) models for classifying cognitive load using data obtained from fNIRS devices. Those studies were analyzed based on type of feature selection methods, input, and DL model architectures. Most of the existing cognitive load studies are based on ML algorithms, which follow signal filtration and hand-crafted features. It is observed that hybrid DL architectures that integrate convolution and LSTM operators performed significantly better in comparison with other models. However, DL models especially hybrid models have not been extensively investigated for the classification of cognitive load captured by fNIRS devices. The current trends and challenges are highlighted to provide directions for the development of DL models pertaining to fNIRS research.
format	Article
author	Khan, Mehshan Ahmed Asadi, Houshyar Zhang, Li Qazani, Mohammad Reza Chalak Oladazimi, Sam Loo, Chu Kiong Lim, Chee Peng Nahavandi, Saeid
author_facet	Khan, Mehshan Ahmed Asadi, Houshyar Zhang, Li Qazani, Mohammad Reza Chalak Oladazimi, Sam Loo, Chu Kiong Lim, Chee Peng Nahavandi, Saeid
author_sort	Khan, Mehshan Ahmed
title	Application of artificial intelligence in cognitive load analysis using functional near-infrared spectroscopy: A systematic review
title_short	Application of artificial intelligence in cognitive load analysis using functional near-infrared spectroscopy: A systematic review
title_full	Application of artificial intelligence in cognitive load analysis using functional near-infrared spectroscopy: A systematic review
title_fullStr	Application of artificial intelligence in cognitive load analysis using functional near-infrared spectroscopy: A systematic review
title_full_unstemmed	Application of artificial intelligence in cognitive load analysis using functional near-infrared spectroscopy: A systematic review
title_sort	application of artificial intelligence in cognitive load analysis using functional near-infrared spectroscopy: a systematic review
publisher	Elsevier
publishDate	2024
url	http://eprints.um.edu.my/45399/ https://doi.org/10.1016/j.eswa.2024.123717
_version_	1814047553422884864
score	13.211869

Application of artificial intelligence in cognitive load analysis using functional near-infrared spectroscopy: A systematic review

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