Book chapter
Using Brain Activity to Predict Task Performance and Operator Efficiency
Advances in Brain Inspired Cognitive Systems, pp 147-155
2012
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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23 citations in Scopus
Details
- Title
- Using Brain Activity to Predict Task Performance and Operator Efficiency
- Creators
- Hasan Ayaz - Drexel University, School of Biomedical Engineering, Science, and Health SystemsScott Bunce - Penn State Milton S. Hershey Medical CenterPatricia Shewokis - Drexel University, School of Biomedical Engineering, Science, and Health SystemsKurtulus Izzetoglu - Drexel University, School of Biomedical Engineering, Science, and Health SystemsBen Willems - William J. Hughes Technical Center for Advanced AerospaceBanu Onaral - Drexel University, School of Biomedical Engineering, Science, and Health Systems
- Publication Details
- Advances in Brain Inspired Cognitive Systems, pp 147-155
- Conference
- 5th International Conference, BICS 2012, 5th (Shenyang, Liaoning, China, 11 Jul 2012–14 Jul 2012)
- Series
- Lecture Notes in Computer Science; 7366
- Publisher
- Springer Berlin Heidelberg; Berlin, Heidelberg
- Number of pages
- 9
- Resource Type
- Book chapter
- Language
- English
- Academic Unit
- School of Biomedical Engineering, Science, and Health Systems; School of Education; Nutrition Sciences; Health Sciences Division
- Scopus ID
- 2-s2.0-84865276880
- Other Identifier
- 991014877839204721