Using brain activity to predict task performance and operator efficiency

Hasan Ayaz, Scott Bunce, Patricia Shewokis, Kurtulus Izzetoglu, Ben Willems, Banu Onaral

Research output: Chapter in Book/Report/Conference proceedingConference contribution

18 Scopus citations

Abstract

The efficiency and safety of many complex human-machine systems are closely related to the cognitive workload and situational awareness of their human operators. In this study, we utilized functional near infrared (fNIR) spectroscopy to monitor anterior prefrontal cortex activation of experienced operators during a standard working memory and attention task, the n-back. Results indicated that task efficiency can be estimated using operator's fNIR and behavioral measures together. Moreover, fNIR measures had more predictive power than behavioral measures for estimating operator's future task performance in higher difficulty conditions.

Original languageEnglish (US)
Title of host publicationAdvances in Brain Inspired Cognitive Systems - 5th International Conference, BICS 2012, Proceedings
Pages147-155
Number of pages9
DOIs
StatePublished - Aug 27 2012
Event5th International Conference on Advances in Brain Inspired Cognitive Systems, BICS 2012 - Shenyang, China
Duration: Jul 11 2012Jul 14 2012

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume7366 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other5th International Conference on Advances in Brain Inspired Cognitive Systems, BICS 2012
CountryChina
CityShenyang
Period7/11/127/14/12

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

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    Ayaz, H., Bunce, S., Shewokis, P., Izzetoglu, K., Willems, B., & Onaral, B. (2012). Using brain activity to predict task performance and operator efficiency. In Advances in Brain Inspired Cognitive Systems - 5th International Conference, BICS 2012, Proceedings (pp. 147-155). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7366 LNAI). https://doi.org/10.1007/978-3-642-31561-9_16