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 journalArticlepeer-review

20 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 languageEnglish (US)
Pages (from-to)367-381
Number of pages15
JournalComputer Methods and Programs in Biomedicine
Volume107
Issue number3
DOIs
StatePublished - Sep 1 2012
Externally publishedYes

Keywords

  • Control charts
  • Electroencephalogram
  • Electrooculogram
  • Psychophysiological data
  • Statistical process control

ASJC Scopus subject areas

  • Software
  • Computer Science Applications
  • Health Informatics

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