@article{ref1,
title="A deep learning scheme for mental workload classification based on restricted Boltzmann machines",
journal="Cognition, technology and work",
year="2017",
author="Zhang, Jianhua and Li, Sunan",
volume="19",
number="4",
pages="607-631",
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.<p /> <p>Language: en</p>",
language="en",
issn="1435-5558",
doi="10.1007/s10111-017-0430-6",
url="http://dx.doi.org/10.1007/s10111-017-0430-6"
}