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.

估计认知负荷水平是认知神经科学领域的一个新兴研究课题，因为参与者的表现很大程度上受到认知超负荷或负荷不足结果的影响。借助机器学习或深度学习技术，脑电图 (EEG)、功能磁共振成像、功能近红外光谱、呼吸活动和眼睛活动等不同的生理测量可有效地用于估计工作负荷水平。一些评论仅关注使用机器学习分类器或不同生理测量的多模态融合进行工作量估计的基于脑电图的工作量估计。然而，仍然需要对用于估计认知负荷水平的所有生理测量进行详细分析。因此，这项调查强调了对评估认知负荷的所有生理指标的深入分析。这项调查强调了认知工作量的基础知识、开放获取数据集、认知任务的实验范式以及估计工作量水平的不同措施。最后，我们强调本次审查的重要发现并确定了开放的挑战。此外，我们还指定了研究人员克服这些挑战的未来范围。