Journal article
Detection of temporal changes in psychophysiological data using statistical process control methods
Computer methods and programs in biomedicine, Vol.107(3), pp.367-381
09/2012
DOI: 10.1016/j.cmpb.2011.01.003
PMID: 21377752
Abstract
We consider the problem of detecting temporal changes in the functional state of human subjects due to varying levels of cognitive load using real-time psychophysiological data. The proposed approach relies on monitoring several channels of electroencephalogram (EEG) and electrooculogram (EOG) signals using the methods of statistical process control. It is demonstrated that control charting methods are capable of detecting changes in psychophysiological signals that are induced by varying cognitive load with high accuracy and low false alarm rates, and are capable of accommodating subject-specific differences while being robust with respect to differences between different trials performed by the same subject.
Details
- Title: Subtitle
- Detection of temporal changes in psychophysiological data using statistical process control methods
- Creators
- Jordan Cannon - Department of Mechanical and Industrial Engineering, University of Iowa, 3131 Seamans Center, Iowa City, IA 52242, USAPavlo A Krokhmal - Department of Mechanical and Industrial Engineering, University of Iowa, 3131 Seamans Center, Iowa City, IA 52242, USAYong Chen - Department of Mechanical and Industrial Engineering, University of Iowa, 3131 Seamans Center, Iowa City, IA 52242, USARobert Murphey - Air Force Research Lab, Munitions Directorate, 101 West Eglin Bvld, Eglin AFB, FL 32542, USA
- Resource Type
- Journal article
- Publication Details
- Computer methods and programs in biomedicine, Vol.107(3), pp.367-381
- Publisher
- Elsevier Ireland Ltd
- DOI
- 10.1016/j.cmpb.2011.01.003
- PMID
- 21377752
- ISSN
- 0169-2607
- eISSN
- 1872-7565
- Grant note
- name: National Science Foundation's Graduate Research Fellowship; DOI: 10.13039/100000181, name: Air Force Office of Scientific Research
- Language
- English
- Date published
- 09/2012
- Academic Unit
- Industrial and Systems Engineering
- Record Identifier
- 9984064588802771
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