Human Cyber-Physical Systems Laboratory, Florida International University, Miami, FL 33174, USA.
Sensors (Basel). 2020 Sep 7;20(18):5082. doi: 10.3390/s20185082.
Functional Near-Infrared Spectroscopy (fNIRS) is a hemodynamic modality in human cognitive workload assessment receiving popularity due to its easier implementation, non-invasiveness, low cost and other benefits from the signal-processing point of view. Wearable wireless fNIRS systems used in research have promisingly shown that fNIRS could be used in cognitive workload assessment in out-of-the-lab scenarios, such as in operators' cognitive workload monitoring. In such a scenario, the wearability of the system is a significant factor affecting user comfort. In this respect, the wearability of the system can be improved if it is possible to minimize an fNIRS system without much compromise of the cognitive workload detection accuracy. In this study, cognitive workload-related hemodynamic changes were acquired using an fNIRS system covering the whole forehead, which is the region of interest in most cognitive workload-monitoring studies. A machine learning approach was applied to explore how the mean accuracy of the cognitive workload classification accuracy varied across various sensing locations on the forehead such as the Left, Mid, Right, Left-Mid, Right-Mid and Whole forehead. The statistical significance analysis result showed that the Mid location could result in significant cognitive workload classification accuracy compared to Whole forehead sensing, with a statistically insignificant difference in the mean accuracy. Thus, the wearable fNIRS system can be improved in terms of wearability by optimizing the sensor location, considering the sensing of the Mid location on the forehead for cognitive workload monitoring.
功能性近红外光谱（fNIRS）是一种人类认知负荷评估的血液动力学模态，由于其易于实现、非侵入性、低成本以及从信号处理角度的其他优势，越来越受到关注。用于研究的可穿戴无线 fNIRS 系统已经有希望表明，fNIRS 可以用于非实验室场景中的认知负荷评估，例如在操作人员的认知负荷监测中。在这种情况下，系统的可穿戴性是影响用户舒适度的一个重要因素。在这方面，如果有可能在不大大降低认知负荷检测准确性的情况下最小化 fNIRS 系统，那么系统的可穿戴性就可以得到改善。在这项研究中，使用覆盖整个前额的 fNIRS 系统获取与认知负荷相关的血液动力学变化，这是大多数认知负荷监测研究中的感兴趣区域。应用机器学习方法来探索认知负荷分类准确性的平均准确率如何在前额的各个感应位置（如左、中、右、左中、右中和整个前额）之间变化。统计意义分析结果表明，与整个前额感应相比，中间位置可以导致显著的认知负荷分类准确性，并且在平均准确率方面没有统计学差异。因此，考虑到在前额的中间位置进行认知负荷监测，可以通过优化传感器位置来提高可穿戴 fNIRS 系统的可穿戴性。
