Cognitive workload estimation using physiological measures: a review

被引：22

作者：

Das Chakladar, Debashis ^{[1
]}

Roy, Partha Pratim ^{[1
]}

机构：

[1] Indian Inst Technol Roorkee, Dept Comp Sci & Engn, Roorkee, Uttarakhand, India

来源：

COGNITIVE NEURODYNAMICS | 2024年 / 18卷 / 04期

关键词：

Electroencephalography; Cognitive workload; Convolutional neural network; Long short-term memory; ARTIFICIAL NEURAL-NETWORK; MENTAL WORKLOAD; EEG SIGNALS; EYE ACTIVITY; TASK; CLASSIFICATION; MEMORY; LOAD; STRESS; PERFORMANCE;

D O I：

10.1007/s11571-023-10051-3

中图分类号：

Q189 [神经科学];

学科分类号：

071006 ;

摘要：

Estimating cognitive workload levels is an emerging research topic in the cognitive neuroscience domain, as participants' performance is highly influenced by cognitive overload or underload results. Different physiological measures such as Electroencephalography (EEG), Functional Magnetic Resonance Imaging, Functional near-infrared spectroscopy, respiratory activity, and eye activity are efficiently used to estimate workload levels with the help of machine learning or deep learning techniques. Some reviews focus only on EEG-based workload estimation using machine learning classifiers or multimodal fusion of different physiological measures for workload estimation. However, a detailed analysis of all physiological measures for estimating cognitive workload levels still needs to be discovered. Thus, this survey highlights the in-depth analysis of all the physiological measures for assessing cognitive workload. This survey emphasizes the basics of cognitive workload, open-access datasets, the experimental paradigm of cognitive tasks, and different measures for estimating workload levels. Lastly, we emphasize the significant findings from this review and identify the open challenges. In addition, we also specify future scopes for researchers to overcome those challenges.

引用

页码：1445 / 1465

页数：21

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