Identifying Changes in Human Operator Mental Workload by Locally Linear Embedding and Support Vector Clustering Approaches

Abstract

This paper describes a psychophysiological-signal-based clustering framework for detecting the changes in operator mental workload incurred by a simulated process control task. A combination of locally linear embedding and support vector clustering approaches is adopted. The unsupervised method is shown to be able to extract features from several channels of the electroencephalogram (EEG) data and to determine whether or not the level of mental workload changes. Simulation results have also demonstrated that a few data clusters can be derived to interpret the change in the operator workload.