文献类型：期刊文献

英文题名：Cross-subject mental workload classification using kernel spectral regression and transfer learning techniques

作者：Zhang, Jianhua[1];Wang, Yongcun[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：587

外文期刊名：COGNITION TECHNOLOGY & WORK

收录：;EI(收录号：20172803926276);WOS:【SSCI(收录号:WOS:000417269600005)，SCI-EXPANDED(收录号:WOS:000417269600005)】；

基金：The authors would like to thank the developers of the aCAMS software and Dr Zhong Yin for his helpful discussion. The work was supported in part by the National Natural Science Foundation of China under Grant No. 61075070 and Key Grant No. 11232005.

语种：英文

外文关键词：Pattern classification; Human-machine system; Mental workload; Dimensionality reduction; Transfer learning; Kernel method

摘要：Due to the poor generalizability of the subject-specific mental workload (MWL) classifier, we propose a cross-subject MWL recognition framework in this paper. Firstly, we use fuzzy mutual information-based wavelet-packet transform (FMI-WPT) technique to extract the salient physiological features of the MWL. Then, we combine kernel spectral regression (KSR) and transferable discriminative dimensionality reduction (TDDR) methods to reduce the dimensionality of the feature vector and to transfer the classifier model across subjects. Finally, the measured data analysis results are presented to show the enhanced performance of the proposed framework for multi-class MWL recognition.