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Proceedings Paper

Evaluation of an EEG workload model in an Aegis simulation environment
Author(s): 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
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Paper Abstract

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, hooking-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.

Paper Details

Date Published: 23 May 2005
PDF: 10 pages
Proc. SPIE 5797, Biomonitoring for Physiological and Cognitive Performance during Military Operations, (23 May 2005); doi: 10.1117/12.598555
Show Author Affiliations
Chris Berka, Advanced Brain Monitoring, Inc. (United States)
Daniel J. Levendowski, Advanced Brain Monitoring, Inc. (United States)
Caitlin K. Ramsey, Advanced Brain Monitoring, Inc. (United States)
Gene Davis, Advanced Brain Monitoring, Inc. (United States)
Michelle N. Lumicao, Advanced Brain Monitoring, Inc. (United States)
Kay Stanney, Univ. of Central Florida (United States)
Leah Reeves, Univ. of Central Florida (United States)
Susan Harkness Regli, Lockheed Martin Advanced Technology Labs. (United States)
Patrice D. Tremoulet, Lockheed Martin Advanced Technology Labs. (United States)
Kathleen Stibler, Lockheed Martin Advanced Technology Labs. (United States)


Published in SPIE Proceedings Vol. 5797:
Biomonitoring for Physiological and Cognitive Performance during Military Operations
John A. Caldwell; Nancy Jo Wesensten, Editor(s)

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