Evaluation of an EEG-workload model in the Aegis simulation environment

Chris Berka, Daniel J. Levendowski, Caitlin K. Ramsey, Gene Davis, Michelle N. Lumicao, Kay Stanney, Leah Reeves, Susan Harkness Regli, Patrice D. Tremoulet, Kathleen Stibler

Research output: Contribution to journalConference articlepeer-review

59 Scopus citations


The integration of real-time electroencephalogram (EEG) workload indices into the man-machine interface could greatly enhance performance of complex tasks, transforming traditionally passive human-system interaction (HSI) into an active exchange where physiological indicators adjust the interaction to suit a user's engagement level. The envisioned outcome is a closed-loop system that utilizes EEG and other physiological indices for dynamic regulation and optimization of HSI in real-time. As a first step towards a closed-loop system, five individuals performed as identification supervisors (IDSs) in an Aegis command and control (C2) simulated environment, a combat system with advanced, automatic detect-and-track, multi-function phased array radar. The Aegis task involved monitoring multiple data sources (i.e., missile-tracks, alerts, queries, resources), detecting required actions, responding appropriately, and ensuring system status remains within desired parameters. During task operation, a preliminary workload measure calculated in real-time for each second of EEG and was used to manipulate the Aegis task demands. In post-hoc analysis, the use of a five-level workload measure to detect cognitively challenging events was evaluated. Events in decreasing order of difficulty were track selection-identification, alert-responses, booking-tracks, and queries. High/extreme EEG-workload occurred during high cognitive-load tasks with a detection efficiency approaching 100% for selection-identification and alert-responses, 77% for hooking-tracks and 70% for queries. Over 95% of high/extreme EEG-workload across participants occurred during high-difficulty events (false positive rate < 5%). The high/extreme workload occurred between 25-30% of time. These results suggest an intelligent closed-loop system incorporating EEG-workload measures could be designed to re-allocate tasks and aid in efficiently streamlining a user's cognitive workload. Such an approach could ensure the operator remains uninterrupted during high/extreme workload periods, thereby resulting in increased productivity and reduced errors.

Original languageEnglish (US)
Article number14
Pages (from-to)90-99
Number of pages10
JournalProceedings of SPIE - The International Society for Optical Engineering
StatePublished - 2005
Externally publishedYes
EventBiomonitoring for Physiological and Cognitive Performance during Military Operations - Orlando, FL, United States
Duration: Mar 31 2005Apr 1 2005

All Science Journal Classification (ASJC) codes

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering


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