Multisubject "Learning" for Mental Workload Classification Using Concurrent EEG, fNIRS, and Physiological Measures

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.