You are here

NeuroAdapt II

Award Information
Agency: Department of Defense
Branch: Air Force
Contract: FA8649-22-P-0734
Agency Tracking Number: FX20C-TCSO1-0278
Amount: $750,000.00
Phase: Phase II
Program: STTR
Solicitation Topic Code: AF20C-TCSO1
Solicitation Number: X20.C
Solicitation Year: 2020
Award Year: 2022
Award Start Date (Proposal Award Date): 2022-06-22
Award End Date (Contract End Date): 2023-09-22
Small Business Information
12 Gill Street Suite 1400
Woburn, MA 01801-1111
United States
DUNS: 967259946
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 Jonathan Drucker
 (813) 766-0527
Business Contact
 Thomas McKenna
Phone: (781) 496-2443
Research Institution
 University of Chicago
 Howard Nusbaum
Chicago Center for Practical Wisdom, 5848 South University Avenue
Chicago, IL 60637-1554
United States

 (773) 702-6468
 Nonprofit College or University

Air Traffic Control (ATC) is a vital function for the US Air Force, ensuring the safety and efficiency of aircrew and ground personnel. ATC is cognitively demanding, sometimes likened to solving a puzzle: operators must actively track the three-dimensional position, speed, and attitude of multiple aircraft simultaneously, all while maintaining awareness of their destinations and objectives. This is a logistical problem requiring sharply honed cognitive skills in the domains of working memory, executive function, attention, spatial cognition, and stress management, as well as a social problem in which ATC operators must communicate effectively with pilots in their airspace to maintain collective situational awareness and ensure safe and efficient flow of traffic.  Air Force ATC operators receive only 72 days of technical training. Therefore, even incremental improvements to effectiveness and efficiency of ATC training programs can have a significant impact; advanced solutions are needed to enhance and accelerate ATC training to maximize mission readiness and to reduce personnel and material costs. Adaptive learning methods are evolving rapidly to meet these needs by tailoring training to the abilities of the learner. Adaptive models infer an individual’s current skill level and intelligently select training materials to help them progress to more advanced stages. The models are informed by behavioral data, that is, by performance on a training task. They do not, however, take into account the learner’s neurophysiological state. Especially in such a mentally demanding domain as ATC, neurophysiological biomarkers corresponding with cognitive and affective states are needed to provide crucial context, enabling deeper insights that generate recommendations for more optimal training policies.  To address these challenges, Aptima, Inc. the University of Chicago, and Reflexion propose NeuroAdapt, a brain-computer interface that incorporates neurophysiological signals into an adaptive learning framework, enhancing motor and cognitive skills for elite skill training. NeuroAdapt fuses training performance data with rich neurophysiological data to derive deep insights into the learner’s cognitive state. Aptima’s ML-backed adaptive learning algorithms then use this information to control training parameters in real time, tailoring the paradigm to the unique needs of the individual learner. We have devised a theory-based, data-driven approach to measure EEG, EKG, and other neurophysiological signals while the trainee is actively using the system. 

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

US Flag An Official Website of the United States Government