Using brain activity to predict task performance and operator efficiency

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

doi:10.1007/978-3-642-31561-9_16

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 proceeding › Conference contribution

21 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 language	English (US)
Title of host publication	Advances in Brain Inspired Cognitive Systems - 5th International Conference, BICS 2012, Proceedings
Pages	147-155
Number of pages	9
DOIs	https://doi.org/10.1007/978-3-642-31561-9_16
State	Published - 2012
Event	5th International Conference on Advances in Brain Inspired Cognitive Systems, BICS 2012 - Shenyang, China Duration: Jul 11 2012 → Jul 14 2012

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	7366 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Other

Other	5th International Conference on Advances in Brain Inspired Cognitive Systems, BICS 2012
Country/Territory	China
City	Shenyang
Period	7/11/12 → 7/14/12

All Science Journal Classification (ASJC) codes

Theoretical Computer Science
General Computer Science

Access to Document

10.1007/978-3-642-31561-9_16

Cite this

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

Ayaz, Hasan ; Bunce, Scott ; Shewokis, Patricia et al. / Using brain activity to predict task performance and operator efficiency. Advances in Brain Inspired Cognitive Systems - 5th International Conference, BICS 2012, Proceedings. 2012. pp. 147-155 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "Using brain activity to predict task performance and operator efficiency",

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.",

author = "Hasan Ayaz and Scott Bunce and Patricia Shewokis and Kurtulus Izzetoglu and Ben Willems and Banu Onaral",

year = "2012",

doi = "10.1007/978-3-642-31561-9_16",

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series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

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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. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 7366 LNAI, pp. 147-155, 5th International Conference on Advances in Brain Inspired Cognitive Systems, BICS 2012, Shenyang, China, 7/11/12. https://doi.org/10.1007/978-3-642-31561-9_16

Using brain activity to predict task performance and operator efficiency. / Ayaz, Hasan; Bunce, Scott; Shewokis, Patricia et al.
Advances in Brain Inspired Cognitive Systems - 5th International Conference, BICS 2012, Proceedings. 2012. p. 147-155 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7366 LNAI).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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T1 - Using brain activity to predict task performance and operator efficiency

AU - Ayaz, Hasan

AU - Bunce, Scott

AU - Shewokis, Patricia

AU - Izzetoglu, Kurtulus

AU - Willems, Ben

AU - Onaral, Banu

PY - 2012

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N2 - 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.

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

Using brain activity to predict task performance and operator efficiency

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