Detection of temporal changes in psychophysiological data using statistical process control methods

Authors:
Jordan Cannon;Pavlo A. Krokhmal;Yong Chen;Robert Murphey
Affiliations:
Department of Mechanical and Industrial Engineering, University of Iowa, 3131 Seamans Center, Iowa City, IA 52242, USA;Department of Mechanical and Industrial Engineering, University of Iowa, 3131 Seamans Center, Iowa City, IA 52242, USA;Department of Mechanical and Industrial Engineering, University of Iowa, 3131 Seamans Center, Iowa City, IA 52242, USA;Air Force Research Lab, Munitions Directorate, 101 West Eglin Bvld, Eglin AFB, FL 32542, USA
Venue:
Computer Methods and Programs in Biomedicine
Year:
2012

Citing 2
Cited 1

A multivariate exponentially weighted moving average control chart

Technometrics
Signals, Systems, and Transforms

Signals, Systems, and Transforms

Operator functional state classification using least-square support vector machine based recursive feature elimination technique

Computer Methods and Programs in Biomedicine

Quantified Score

Hi-index	0.00

Visualization

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.