Title

Evaluation Of An Eeg-Workload Model In The Aegis Simulation Environment

Keywords

Closed-loop system; Cognitive overload; Command-and-control systems (C ) 2; EEG; Human-computer interface; Information delivery; Mitigation strategies; Task allocation; Workload

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, 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.

Publication Date

11-9-2005

Publication Title

Proceedings of SPIE - The International Society for Optical Engineering

Volume

5797

Number of Pages

90-99

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

DOI Link

https://doi.org/10.1117/12.601927

Socpus ID

27544479429 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/27544479429

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