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Using Brain Activity to Predict Task Performance and Operator Efficiency

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Advances in Brain Inspired Cognitive Systems (BICS 2012)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 7366))

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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.

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Author information

Authors and Affiliations

  1. School of Biomedical Engineering, Science & Health Systems, Drexel University, Philadelphia, PA, 19104, USA

    Hasan Ayaz, Patricia Shewokis, Kurtulus Izzetoglu & Banu Onaral

  2. Cognitive Neuroengineering and Quantitative Experimental Research, (CONQUER) Collaborative, Drexel University, Philadelphia, PA, 19104, USA

    Hasan Ayaz, Scott Bunce, Patricia Shewokis, Kurtulus Izzetoglu & Banu Onaral

  3. Penn State Hershey Medical Center and Penn State College of Medicine, Hershey, PA, 17003, USA

    Scott Bunce

  4. College of Nursing and Health Professions, Drexel University, Philadelphia, PA, 19102, USA

    Patricia Shewokis

  5. Federal Aviation Administration William J. Hughes Technical Center, Atlantic City International Airport, NJ, USA

    Ben Willems

Authors
  1. Hasan Ayaz
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  2. Scott Bunce
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  3. Patricia Shewokis
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  4. Kurtulus Izzetoglu
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  5. Ben Willems
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  6. Banu Onaral
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Editor information

Editors and Affiliations

  1. College of Information Science and Engineering, Northeastern University, 110004, Shenyang, P.R. China

    Huaguang Zhang

  2. Dept. of Computing Science and Mathematics, University of Stirling, FK9 4LA, Stirling, Scotland, UK

    Amir Hussain

  3. Institute of Automation, Chinese Academy of Sciences, 100190, Beijing, China

    Derong Liu

  4. Northeastern University, School of Information Science and Engineering, 110004, Shenyang, Liaoning, People’s Republic of China

    Zhanshan Wang

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© 2012 Springer-Verlag Berlin Heidelberg

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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: Zhang, H., Hussain, A., Liu, D., Wang, Z. (eds) Advances in Brain Inspired Cognitive Systems. BICS 2012. Lecture Notes in Computer Science(), vol 7366. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31561-9_16

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  • DOI: https://doi.org/10.1007/978-3-642-31561-9_16

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-31560-2

  • Online ISBN: 978-3-642-31561-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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