文献类型：期刊文献

英文题名：Cross-subject recognition of operator functional states via EEG and switching deep belief networks with adaptive weights

作者：Yin, Zhong[1];Zhang, Jianhua[2]

机构：[1]Univ Shanghai Sci & Technol, Shanghai Key Lab Modern Opt Syst, Minist Educ, Engn Res Ctr Opt Instrument & Syst, Jungong Rd 516, Shanghai 200093, Peoples R China;[2]East China Univ Sci & Technol, Dept Automat, Shanghai 200237, Peoples R China

年份：2017

卷号：260

起止页码：349

外文期刊名：NEUROCOMPUTING

收录：;EI(收录号：20172203701195);WOS:【SCI-EXPANDED(收录号:WOS:000405536900037)】；

基金：This work is sponsored by the Shanghai Sailing Program (17YF1427000), and the National Natural Science Foundation of China under Grant Nos. 61673276, 11502145, and 61603256.

语种：英文

外文关键词：Operator functional states; Mental workload; Mental fatigue; Electroencephalogram; Deep learning; Deep belief networks

摘要：Assessing operator functional states (OFS) by using neurophysiological signals can provide continuous prediction of instantaneous human performance in safety-critical human-machine systems. Most existing OFS recognizers were built via a subject-dependent manner, where a new model has to be trained based on historical physiological data of the same subject. The main objective of this paper is to generalize such paradigm to cross-subject OFS recognition by exploiting the new improvements in deep learning principles. To this end, we propose a novel EEG-based OFS classifier, switching deep belief networks with adaptive weights (SDBN), which is generic for detecting variations of mental workload, mental fatigue, and the coupling effect of the two variables across multiple subjects. The temporal OFS is predicted by switching the ensembles of the static and adaptive DBNs at each time step via a Gaussian-kernel based criterion. The results comparison demonstrates that the SDBN not only significantly improves classification accuracy but also has the capability to distinguish multiple dimensions in OFS. (C) 2017 Elsevier B.V. All rights reserved.