Liu Yichuan, Ayaz Hasan, Shewokis Patricia A
School of Biomedical Engineering, Science and Health Systems, Drexel UniversityPhiladelphia, PA, United States.
Cognitive Neuroengineering and Quantitative Experimental Research Collaborative, Drexel UniversityPhiladelphia, PA, United States.
Front Hum Neurosci. 2017 Jul 27;11:389. doi: 10.3389/fnhum.2017.00389. eCollection 2017.
An accurate measure of mental workload level has diverse neuroergonomic applications ranging from brain computer interfacing to improving the efficiency of human operators. In this study, we integrated electroencephalogram (EEG), functional near-infrared spectroscopy (fNIRS), and physiological measures for the classification of three workload levels in an n-back working memory task. A significantly better than chance level classification was achieved by EEG-alone, fNIRS-alone, physiological alone, and EEG+fNIRS based approaches. The results confirmed our previous finding that integrating EEG and fNIRS significantly improved workload classification compared to using EEG-alone or fNIRS-alone. The inclusion of physiological measures, however, does not significantly improves EEG-based or fNIRS-based workload classification. A major limitation of currently available mental workload assessment approaches is the requirement to record lengthy calibration data from the target subject to train workload classifiers. We show that by learning from the data of other subjects, workload classification accuracy can be improved especially when the amount of data from the target subject is small.
准确测量心理负荷水平在神经工效学领域有着广泛的应用，涵盖从脑机接口到提高人类操作员效率等多个方面。在本研究中，我们整合了脑电图（EEG）、功能近红外光谱（fNIRS）以及生理测量方法，用于在n-back工作记忆任务中对三种负荷水平进行分类。单独使用EEG、单独使用fNIRS、单独使用生理测量以及基于EEG+fNIRS的方法都实现了显著高于随机水平的分类。结果证实了我们之前的发现，即与单独使用EEG或fNIRS相比，整合EEG和fNIRS能显著提高负荷分类效果。然而，纳入生理测量并未显著改善基于EEG或fNIRS的负荷分类。当前可用的心理负荷评估方法的一个主要局限性是需要从目标受试者记录冗长的校准数据来训练负荷分类器。我们表明，通过从其他受试者的数据中学习，可以提高负荷分类准确率，尤其是当来自目标受试者的数据量较少时。
