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A Software Toolkit for Predicting the Neural Signatures of Cognitive States

Award Information
Agency: Department of Defense
Branch: Air Force
Contract: FA8649-20-C-0009
Agency Tracking Number: F18B-001-0119
Amount: $749,435.00
Phase: Phase II
Program: STTR
Solicitation Topic Code: AF18B-T001
Solicitation Number: 18.B
Solicitation Year: 2018
Award Year: 2020
Award Start Date (Proposal Award Date): 2020-01-17
Award End Date (Contract End Date): 2022-01-17
Small Business Information
215 Parkway North P.O. Box 280
Waterford, CT 06385
United States
DUNS: 077317766
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 James Mccarthy
 Principal Investigator
 (937) 429-9711
Business Contact
 Joyce Brown
Phone: (860) 326-3768
Research Institution
 Miami University, Department of Psychology
 Mr. James Oris Mr. James Oris
102 Roudebush Hall 501 E. High Street
Oxford, OH 45056
United States

 (513) 529-3600
 Nonprofit College or University

The United States Air Force (USAF) has a long history of using human performance models to increase the effectiveness of training, and predict the impact of physical factors (like fatigue) and environmental factors (like time pressures and information uncertainty). Within Phase I, Sonalysts and Miami University worked to improve the quality of these models through the development of the EEG Modeling Architecture for Predicting States (EMAPS) tool. EMAPS is part of a growing collection of research that merges the mathematical and neurophysiological modeling approaches. EMAPS will enhance USAF modeling in several ways. First, as the military increasingly moves toward human-machine teaming, developers can use these models to create more effective synthetic teammates. Second, in training contexts, developers can use the models to create more realistic constructive teammates, adversaries, or even teachers. Third, this capability can be used to more reliably assess system design and predict human performance in advanced man-machine systems. Fourth, superior models can be used to drive more responsive adaptive automation systems. Beyond extending the accuracy with which models can make predictions for both individuals and groups, EMAPS will guide the efficiency of modeling efforts by enabling multi-scale modeling for the observed phenomena.

* Information listed above is at the time of submission. *

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