Individual-Specific Classification of Mental Workload Levels Via an Ensemble Heterogeneous Extreme Learning Machine for EEG Modeling

• Published in: Symmetry (Basel) 2019-07, Vol.11 (7), p.944
• Main Authors: Tao, Jiadong, Yin, Zhong, Liu, Lei, Tian, Ying, Sun, Zhanquan, Zhang, Jianhua
• Format: Article
• Language: English
• Subjects: Accuracy; Air traffic control; Algorithms; Artificial neural networks; Automation; Business metrics; Classification; Classifiers; Electrocardiography; Electroencephalography; ensemble learning; extreme learning machine; Human performance; Machine learning; mental workload; Neural networks; Nuclear power plants; Parameter identification; Physiology; Support vector machines; Temporal resolution; Workload; Workloads
• ISSN: 2073-8994; 2073-8994
• DOI: 10.3390/sym11070944

In a human–machine cooperation system, assessing the mental workload (MW) of the human operator is quite crucial to maintaining safe operation conditions. Among various MW indicators, electroencephalography (EEG) signals are particularly attractive because of their high temporal resolution and sensitivity to the occupation of working memory. However, the individual difference of the EEG feature distribution may impair the machine-learning based MW classifier. In this paper, we employed a fast-training neural network, extreme learning machine (ELM), as the basis to build an individual-specific classifier ensemble to recognize binary MW. To improve the diversity of the classification committee, heterogeneous member classifiers were adopted by fusing multiple ELMs and Bayesian models. Specifically, a deep network structure was applied in each weak model aiming at finding informative EEG feature representations. The structure of hyper-parameters of the proposed heterogeneous ensemble ELM (HE-ELM) was then identified and then its performance was compared against several competitive MW classifiers. We found that the HE-ELM model was superior for improving the individual-specific accuracy of MW assessments.