An algorithm for online detection of temporal changes in operator cognitive state using real-time psychophysiological data

Jordan A. Cannon, Pavlo A. Krokhmal, Russell V. Lenth, Robert Murphey

Research output: Contribution to journalArticle

19 Scopus citations

Abstract

We consider the problem of on-the-fly detection of temporal changes in the cognitive state of human subjects due to varying levels of difficulty of performed tasks using real-time EEG and EOG data. We construct the Cognitive State Indicator (CSI) as a function that projects the multidimensional EEG/EOG signals onto the interval [0,1] by maximizing the Kullback-Leibler distance between distributions of the signals, and whose values change continuously with variations in cognitive load. During offline testing (i.e., when evolution in time is disregarded) it was demonstrated that the CSI can serve as a statistically significant discriminator between states of different cognitive loads. In the online setting, a trend detection heuristic (TDH) has been proposed to detect real-time changes in the cognitive state by monitoring trends in the CSI. Our results support the application of the CSI and the TDH in future closed-loop control systems with human supervision.

Original languageEnglish (US)
Pages (from-to)229-236
Number of pages8
JournalBiomedical Signal Processing and Control
Volume5
Issue number3
DOIs
StatePublished - Jul 2010
Externally publishedYes

Keywords

  • Cognitive state
  • EEG
  • EOG
  • Kullback-Leibler distance
  • Psychophysiological data
  • Statistical analysis

ASJC Scopus subject areas

  • Signal Processing
  • Health Informatics

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