文献类型：期刊文献

英文题名：Identification of temporal variations in mental workload using locally-linear-embedding-based EEG feature reduction and support-vector-machine-based clustering and classification techniques

作者：Yin, Zhong[1];Zhang, Jianhua[1]

机构：[1]E China Univ Sci & Technol, Dept Automat, Shanghai 200237, Peoples R China

年份：2014

卷号：115

期号：3

起止页码：119

外文期刊名：COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE

收录：;EI(收录号：20142217764816);WOS:【SCI-EXPANDED(收录号:WOS:000336427200003)】；

基金：The authors would like to thank the developers of the ACAMS software which was used in our experiments. The work was supported by the National Natural Science Foundation of China under Grant No. 61075070 and Key Grant No. 11232005. The authors wish to thank Prof. Dr.-Ing. Joerg Raisch (Control Systems Group of Technical University of Berlin, Germany) for his support and helpful comments on the draft version of this paper. The comments of Mr. Yuxin Ma are also gratefully acknowledged.

语种：英文

外文关键词：Mental workload; Locally linear embedding; Support vector data description; Support vector clustering; Operator functional state

摘要：Identifying the abnormal changes of mental workload (MWL) over time is quite crucial for preventing the accidents due to cognitive overload and inattention of human operators in safety-critical human-machine systems. It is known that various neuroimaging technologies can be used to identify the MWL variations. In order to classify MWL into a few discrete levels using representative MWL indicators and small-sized training samples, a novel EEG-based approach by combining locally linear embedding (LLE), support vector clustering (SVC) and support vector data description (SVDD) techniques is proposed and evaluated by using the experimentally measured data. The MWL indicators from different cortical regions are first elicited by using the LLE technique. Then, the SVC approach is used to find the clusters of these MWL indicators and thereby to detect MWL variations. It is shown that the clusters can be interpreted as the binary class MWL. Furthermore, a trained binary SVDD classifier is shown to be capable of detecting slight variations of those indicators. By combining the two schemes, a SVC-SVDD framework is proposed, where the clear-cut (smaller) cluster is detected by SVC first and then a subsequent SVDD model is utilized to divide the overlapped (larger) cluster into two classes. Finally, three-class MWL levels (low, normal and high) can be identified automatically. The experimental data analysis results are compared with those of several existing methods. It has been demonstrated that the proposed framework can lead to acceptable computational accuracy and has the advantages of both unsupervised and supervised training strategies. (C) 2014 Elsevier Ireland Ltd. All rights reserved.