Journal
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
Volume 113, Issue 1, Pages 101-115Publisher
ELSEVIER IRELAND LTD
DOI: 10.1016/j.cmpb.2013.09.007
Keywords
Operator functional state; Adaptive automation; Mental workload; Recursive feature elimination; Support vector machine
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Funding
- National Natural Science Foundation of China [61075070]
- [11232005]
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This paper proposed two psychophysiological-data-driven classification frameworks for operator functional states (OFS) assessment in safety-critical human-machine systems with stable generalization ability. The recursive feature elimination (RFE) and least square support vector machine (LSSVM) are combined and used for binary and multiclass feature selection. Besides typical binary LSSVM classifiers for two-class OFS assessment, two multiclass classifiers based on multiclass LSSVM-RFE and decision directed acyclic graph (DDAG) scheme are developed, one used for recognizing the high mental workload and fatigued state while the other for differentiating overloaded and base-line states from the normal states. Feature selection results have revealed that different dimensions of OFS can be characterized by specific set of psychophysiological features. Performance comparison studies show that reasonable high and stable classification accuracy of both classification frameworks can be achieved if the RFE procedure is properly implemented and utilized. (C) 2013 Elsevier Ireland Ltd. All rights reserved.
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