A deep learning scheme for mental workload classification based on restricted Boltzmann machines

Abstract

The mental workload (MWL) classification is a critical problem for quantitative assessment and analysis of operator functional state in many safety-critical situations with indispensable human–machine cooperation. The MWL can be measured by psychophysiological signals. In this work, we propose a novel restricted Boltzmann machine (RBM) architecture for MWL classification. In relation to this architecture, we examine two main issues: the optimal structure of RBM and selection of the most important EEG channels (electrodes) for MWL classification. The trial-and-error and entropy-based pruning methods are compared for the RBM structure identification. The degree of importance of EEG channels is calculated from the weights in a well-trained network in order to select the most relevant channels for classification task. Extensive comparative results showed that the selected EEG channels lead to accurate MWL classification across subjects.