文献类型：期刊文献

英文题名：A deep learning scheme for mental workload classification based on restricted Boltzmann machines

作者：Zhang, Jianhua[1];Li, Sunan[1]

机构：[1]East China Univ Sci & Technol, Sch Informat Sci & Engn, Shanghai 200237, Peoples R China

年份：2017

卷号：19

期号：4

起止页码：607

外文期刊名：COGNITION TECHNOLOGY & WORK

收录：;EI(收录号：20173604117350);WOS:【SSCI(收录号:WOS:000417269600006)，SCI-EXPANDED(收录号:WOS:000417269600006)】；

基金：This work was supported in part by the National Natural Science Foundation of China under Grant No. 61075070 and Key Grant No. 11232005. The authors would like to thank the developers of the aCAMS software used in our experiments.

语种：英文

外文关键词：Mental workload; Operator functional state; Restricted Boltzmann machine; Deep belief network; Deep learning; Entropy

摘要：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.