文献类型：期刊文献

英文题名：Design of an Adaptive Human-Machine System Based on Dynamical Pattern Recognition of Cognitive Task-Load

作者：Zhang, Jianhua[1];Yin, Zhong[2];Wang, Rubin[3]

机构：[1]East China Univ Sci & Technol, Intelligent Syst Grp, Sch Informat Sci & Engn, Shanghai, Peoples R China;[2]Univ Shanghai Sci & Technol, Sch Opt Elect & Comp Engn, Shanghai, Peoples R China;[3]East China Univ Sci & Technol, Sch Sci, Dept Math, Inst Cognit Neurodynam, Shanghai, Peoples R China

年份：2017

卷号：11

外文期刊名：FRONTIERS IN NEUROSCIENCE

收录：;WOS:【SSCI(收录号:WOS:000396514600001)，SCI-EXPANDED(收录号:WOS:000396514600001)】；

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.

语种：英文

外文关键词：adaptive functional allocation; cognitive task-load; electrophysiology; dynamic pattern recognition; man-machine system

摘要：This paper developed a cognitive task-load (CTL) classification algorithm and allocation strategy to sustain the optimal operator CTL levels over time in safety-critical human-machine integrated systems. An adaptive human-machine system is designed based on a non-linear dynamic CTL classifier, which maps a set of electroencephalogram (EEG) and electrocardiogram (ECG) related features to a few CTL classes. The least-squares support vector machine (LSSVM) is used as dynamic pattern classifier. A series of electrophysiological and performance data acquisition experiments were performed on seven volunteer participants under a simulated process control task environment. The participant-specific dynamic LSSVM model is constructed to classify the instantaneous CTL into five classes at each time instant. The initial feature set, comprising 56 EEG and ECG related features, is reduced to a set of 12 salient features (including 11 EEG-related features) by using the locality preserving projection (LPP) technique. An overall correct classification rate of about 80% is achieved for the 5-class CTL classification problem. Then the predicted CTL is used to adaptively allocate the number of process control tasks between operator and computer-based controller. Simulation results showed that the overall performance of the human-machine system can be improved by using the adaptive automation strategy proposed.