Khan, Mehshan Ahmed, Asadi, Houshyar, Zhang, Li, Chalak Qazani, Mohammad Reza, Oladazimi, Sam, Loo, Chu Kiong, 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 (Part C). 123717.

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Abstract

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, cost-effectiveness, 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.

Item ID:	86704
Item Type:	Article (Research - C1)
ISSN:	0957-4174
Keywords:	Functional Near-InfraRed Spectroscopy (fNIRS)Deep learningMachine learningCognitive loadArtificial intelligence
Copyright Information:	© 2024 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
Funders:	Australian Research Council (ARC)
Projects and Grants:	ARC Project ID: DE210101623
Date Deposited:	10 Sep 2025 02:23
FoR Codes:	46 INFORMATION AND COMPUTING SCIENCES > 4611 Machine learning > 461103 Deep learning @ 50% 40 ENGINEERING > 4009 Electronics, sensors and digital hardware > 400909 Photonic and electro-optical devices, sensors and systems (excl. communications) @ 30% 52 PSYCHOLOGY > 5204 Cognitive and computational psychology > 520499 Cognitive and computational psychology not elsewhere classified @ 20%
SEO Codes:	28 EXPANDING KNOWLEDGE > 2801 Expanding knowledge > 280121 Expanding knowledge in psychology @ 15% 28 EXPANDING KNOWLEDGE > 2801 Expanding knowledge > 280115 Expanding knowledge in the information and computing sciences @ 60% 28 EXPANDING KNOWLEDGE > 2801 Expanding knowledge > 280110 Expanding knowledge in engineering @ 25%
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