文献类型：期刊文献

英文题名：Cross-session classification of mental workload levels using EEG and an adaptive deep learning model

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, Shanghai 200093, Peoples R China;[2]East China Univ Sci & Technol, Dept Automat, Shanghai 200237, Peoples R China

年份：2017

卷号：33

起止页码：30

外文期刊名：BIOMEDICAL SIGNAL PROCESSING AND CONTROL

收录：;EI(收录号：20164803062352);WOS:【SCI-EXPANDED(收录号:WOS:000393726500004)】；

基金：The authors would like to thank the developers of the AutoCAMS software which was used in our experiments. This work is supported by the National Natural Science Foundation of China under Grant No. 61673276, No. 11502145, No. 61603256, the Foundation of Shanghai Municipal Education Commission and the Faculty Innovation Ability Development Project of University of Shanghai for Science and Technology.

语种：英文

外文关键词：Human-machine system; Mental workload; Electroencephalogram (EEG); Deep learning; Operator functional states

摘要：Evaluation of operator Mental Workload (MW) levels via ongoing electroencephalogram (EEG) is quite promising in Human-Machine (HM) collaborative task environment to alarm the temporal operator performance degradation. However, accurate recognition of MW states via a static pattern classifier with training and testing EEG signals recoded on separate days is particularly challenging as EEG features are differently distributed across different sessions. Motivated by the superiority of the deep learning approaches for stable feature abstractions in higher levels, an adaptive Stacked Denoising AutoEncoder (SDAE) is developed to tackling such cross-session MW classification task in which the weights of the shallow hidden neurons could be adaptively updated during the testing procedure. The generalization capability of the adaptive SDAE is first evaluated under within/cross-session conditions. Then, we compare it with the state of the art MW classifiers under different feature selection and the noise corruption paradigms. The results indicate a higher performance of the adaptive SDAE in dealing with the cross-session EEG features. By analyzing the optimal step length, the data augmentation scheme and the computational cost for iterative tuning, the adaptive SDAE is also demonstrated to be acceptable for online implementation. (C) 2016 Elsevier Ltd. All rights reserved.