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

Jordan Cannon; Pavlo A. Krokhmal; Yong Chen; Robert Murphey

doi:10.1016/j.cmpb.2011.01.003

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

Jordan Cannon, Pavlo A. Krokhmal, Yong Chen, Robert Murphey

Research output: Contribution to journal › Article › peer-review

27 Scopus citations

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.

Original language	English (US)
Pages (from-to)	367-381
Number of pages	15
Journal	Computer Methods and Programs in Biomedicine
Volume	107
Issue number	3
DOIs	https://doi.org/10.1016/j.cmpb.2011.01.003
State	Published - Sep 2012
Externally published	Yes

Keywords

Control charts
Electroencephalogram
Electrooculogram
Psychophysiological data
Statistical process control

ASJC Scopus subject areas

Software
Computer Science Applications
Health Informatics

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10.1016/j.cmpb.2011.01.003

Cite this

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title = "Detection of temporal changes in psychophysiological data using statistical process control methods",

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.",

keywords = "Control charts, Electroencephalogram, Electrooculogram, Psychophysiological data, Statistical process control",

author = "Jordan Cannon and Krokhmal, {Pavlo A.} and Yong Chen and Robert Murphey",

note = "Funding Information: J. Cannon acknowledges support from the National Science Foundation's Graduate Research Fellowship ; P. Krokhmal and R. Murphey acknowledge support from the Air Force Office of Scientific Research . ",

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AU - Cannon, Jordan

AU - Krokhmal, Pavlo A.

AU - Chen, Yong

AU - Murphey, Robert

N1 - Funding Information: J. Cannon acknowledges support from the National Science Foundation's Graduate Research Fellowship ; P. Krokhmal and R. Murphey acknowledge support from the Air Force Office of Scientific Research .

PY - 2012/9

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N2 - 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.

AB - 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.

KW - Control charts

KW - Electroencephalogram

KW - Electrooculogram

KW - Psychophysiological data

KW - Statistical process control

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

Abstract

Keywords

ASJC Scopus subject areas

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