Cognitive workload and learning assessment during the implementation of a next-generation air traffic control technology using functional near-infrared spectroscopy

Joshua Harrison, Kurtuluş Izzetoǧlu, Hasan Ayaz, Ben Willems, Sehchang Hah, Ulf Ahlstrom, Hyun Woo, Patricia A. Shewokis, Scott C. Bunce, Banu Onaral

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29 Scopus citations


Neuroimaging technologies, such as functional near-infrared spectroscopy (fNIR), could provide performance metrics directly from brain-based measures to assess safety and performance of operators in high-risk fields. In this paper, we objectively and subjectively examine the cognitive workload of air traffic control specialists utilizing a next-generation conflict resolution advisory. Credible differences were observed between continuously increasing workload levels that were induced by increasing the number of aircraft under control. In higher aircraft counts, a possible saturation in brain activity was realized in the fNIR data. A learning effect was also analyzed across a three-day/nine- session training period. The difference between Day 1 and Day 2 was credible, while there was a noncredible difference between Day 2 and Day 3. The results presented in this paper indicate some advantages in objective measures of cognitive workload assessment with fNIR cortical imaging over the subjective workload assessment keypad.

Original languageEnglish (US)
Article number6826546
Pages (from-to)429-440
Number of pages12
JournalIEEE Transactions on Human-Machine Systems
Issue number4
StatePublished - Aug 2014


All Science Journal Classification (ASJC) codes

  • Human Factors and Ergonomics
  • Control and Systems Engineering
  • Signal Processing
  • Human-Computer Interaction
  • Computer Science Applications
  • Computer Networks and Communications
  • Artificial Intelligence

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