DON26BZ01-NV033 TITLE: Applied Neuroanalytics for Optimization of Naval Training and Operational Readiness
OUSW (R&E) CRITICAL TECHNOLOGY AREA(S): Applied Artificial Intelligence (AAI)
COMPONENT TECHNOLOGY PRIORITY AREA(S): Human-Machine Interfaces;Human-Machine Interfaces
PROJECTED CMMC LEVEL REQUIREMENT: Level 2 (Self)
OBJECTIVE: Develop and demonstrate a neuro-enhanced artificial intelligence (AI) system that captures, analyzes, and operationalizes neurophysiological and behavioral data to provide near real-time, adaptive feedback for improved training efficiency, performance, and operational readiness of U.S. Navy personnel.
DESCRIPTION: The U.S. Navy Force Design 2045 (CNO NavPlan 2024) highlights the importance of the warfighter and human-machine teaming in the future fight, emphasizing the criticality of developing high-performing teams and leaders that are resilient, adaptable, and warrior tough while supporting an increasingly hybrid Fleet of manned assets augmented with thousands of unmanned assets. The future fight will likely require operators to 1) digest and synthesize large amounts of data from an extensive network of humans and machines, 2) make decisions more rapidly due to advances in AI, enhanced connectivity, and autonomous weaponry and 3) oversee a greater number and types of robotics, including swarms (RAND, 2024).
Critical features of this paradigm shift towards manned-unmanned teaming and emphasis on improving warfighter performance are how we train operators. Training is at the forefront of the modernization of Naval operations to enhance readiness and lethality, and this will depend heavily on the cognitive resilience and decision-making capacity of warfighters in these novel, high-stress environments. Traditional training paradigms typically neglect real-time measurement and integration of cognitive and physiological performance states (e.g., mental effort, task engagement, lapses and slips of attention, complacency, mental fatigue, and stress). Emerging technologies for advanced data analytics grounded in neuroscience provide new capability that can enhance warfighter development and mission success by embedding neurofeedback into live and synthetic Naval training environments, providing novel analytical features and data to adapt training in near-real time and accelerate learning at the point of need.
The U.S. Navy seeks to identify a major step forward in neuro-enhanced AI systems to reduce time-to-proficiency and predict Sailor readiness within the unique maritime military environment. This envisioned capability will leverage and further develop Commercial Off-the-Shelf (COTS) neurotechnologies along with complimentary biosensors (e.g., electrocardiography [ECG], electromyography [EMG], eye tracking) and behavioral monitoring tools for Navy-specific use cases to interface with personnel, enabling adaptive and responsive system interaction based on near real-time human state data.
This SBIR topic will prioritize two key demonstrated factors in support of its objective: (1) the ability to collect neural, physiological, and behavioral data in parallel with operators using a desktop or higher fidelity simulator; and (2) the ability to analyze and interact with that data, both in near real-time and post-hoc, using an advanced language-understanding system coupled with an extensive foundational model of the human psychophysiology and/or behavior to provide feedback. This effort will complement existing Navy initiatives, such as those led by NAVAIR, NAWCAD, and NAWCTSD, enhancing existing learning environments through the addition of a brain-based performance layer.
The platform will deliver an autonomous solution for near real-time feedback, improved after-action reporting, and guided adaptation of training scenarios via open data standards that can be used to improve understanding of Sailor state (static and dynamic), which will be imperative for improving warfighter performance and training towards an ever-evolving mission in the future fight.
PHASE I: Design and validate a strategy for integrating the neuro-enhanced AI system with existing Navy training architectures (e.g., NAWCTSD training systems or LVC frameworks). Define and characterize mission-relevant cognitive states predictive of optimal warfighter performance. Develop a system architecture that fuses neurophysiological, behavioral, and mission/environmental data for predictive insight.
Deliver system architecture documentation, a feasibility analysis of neuro-enhanced AI system data integration with Naval training systems, a preliminary data model for cognitive and physiological performance state prediction, and a prototype development roadmap for Phase II.
PHASE II: Build and demonstrate a working prototype of the system integrated within a Navy-relevant training environment. Instrument a Naval operational team (e.g., aircrew, ship bridge) for real-time neurophysiological data collection and adaptive training response. Implement a neuro-enhanced advanced language understanding system for AI-driven coaching, guiding warfighter and instructors in near real-time.
Deliver an Institutional Review Board (IRB) application/approval; a cybersecurity and RMF compliance report; data strategy documentation and integration with existing Navy training platforms; a live data collection event demonstrating improvements in performance and mission readiness; updated data exchange framework using API or Navy-compliant standards.
DON will provide Phase II awardees with the appropriate guidance and assistance for human research protocols though the performer will be responsible for obtaining any required Institutional Review Board (IRB) determinations. IRB determination as well as processing, submission, and review of all paperwork required for human subject use can be a lengthy process. As such, no human research will be allowed without IRB review and work will not be authorized until approval has been obtained, typically as an Option to be exercised during Phase II.
PHASE III DUAL USE APPLICATIONS: Validate system effectiveness for improving warfighter performance and readiness; demonstrate adaptive capabilities with AI-based recommendations; achieve authority to operate (ATO) with Navy training platform(s). Support transition of the SBIR-developed neuro-enhanced AI system.
Validated capabilities will be relevant for:
• Naval Aviation: Enhancing pilot and flight officer readiness and cognitive workload management.
• Surface Fleet Training: Real-time feedback during complex shipboard simulations.
• Submarine Operations: Monitoring mental states during long-duration missions.
• Medical Teams Afloat: Enhancing high-stakes clinical team performance.
• Special Operations Forces: Resilience training and peak performance optimization
Commercial applications include aviation, e-sports, medical simulation, and elite training environments where human performance optimization is critical.
REFERENCES:
KEYWORDS: Neuroanalytics; Human Performance; Predictive Analytics; Brain-Computer Interface (BCI); AI-Enabled Coaching; Real-Time Training Adaptation; Cognitive Load Monitoring; Aircrew Readiness; Adaptive Learning; Large Language Model; NAVAIR; NAWCTSD
| 5/15/26 | Q. | Would the Navy consider complementary approaches that provide a structural baseline of the human operator (e.g., brain, spine, and major joint integrity via advanced imaging) to augment these signals? Such a structural layer could improve interpretation of cognitive/physiologic metrics, enhance readiness prediction, and support individualized training for high-demand populations such as Naval Aviation. |
| A. | We are open to new ideas as long as sufficient rationale is provided in the proposal. | |
| 5/10/26 | Q. | |
| A. | ||
| 5/8/26 | Q. | Phase I scope-fusion architecture, and target neurocognitive states:
The reading-list resources point to a coherent framework: Matthews et al. (2014) establish that workload measures are sensitive but divergent with no general factor; Dehais et al. (2020) map four impaired neurocognitive states (mind wandering, effort withdrawal, perseveration, inattentional phenomena) within a task-engagement x arousal space and propose prefrontal cortex deactivation as a generic impairment index; Armendariz et al. (2025) operationalize Learning Opportunity Zones for adaptive training. To align the Phase I demonstration scope, does the topic anticipate a fusion-oriented architecture (multi-stream HRV + ocular + cortical features feeding a combined readiness index and discriminating among the four impaired states), or is convergent validation of a single physiological gauge against a ground-truth workload label the priority? Asking to confirm the scope before Phase I kickoff. |
| A. | To foster innovation, the Phase I does not prescribe or require one approach over another - proposers to this SBIR should provide a compelling case and justification for their choices on what to target or focus on. Both the validation of single or multi-physiological gauge(s) with individual states and a "fusion-oriented architecture" (multi-modal data stream synthesis) that can discriminate between states are important. | |
| 5/5/26 | Q. | The solicitation lists several use cases in Phase 3. Do proposals need to address multiple/all phase 3 use cases, or is it in scope to select 1 from this list for Phase 1 and 2?
Will ONR facilitate any connections to operational and/or training communities in the Navy, or would performers need to arrange that independently? |
| A. | You can select one use case to focus on. The list provided were examples and is not exhaustive; any Navy-relevant use case is acceptable with appropriate justification. During the Phase I, it is up to the performer to demonstrate the feasibility of their approach (with pre-existing military connections, with civilian groups, and/or available pre-existing data) and during Phase II / III, ONR will facilitate connections for operational integration and testing. |
** TOPIC NOTICE ** |
The Navy Topic above is an "unofficial" copy from the Navy Topics in the DoW FY-26 Release 1 SBIR BAA. Please see the official DoW Topic website at www.dodsbirsttr.mil/submissions/solicitation-documents/active-solicitations for any updates. The DoW issued its Navy FY-26 Release 1 SBIR Topics pre-release on April 13, 2026 which opens to receive proposals on May 6, 2026, and closes June 3, 2026 (12:00pm ET). Direct Contact with Topic Authors: During the pre-release period (April 13, through May 5, 2026) proposing firms have an opportunity to directly contact the Technical Point of Contact (TPOC) to ask technical questions about the specific BAA topic. The TPOC contact information is listed in each topic description. Once DoW begins accepting proposals on May 6, 2026 no further direct contact between proposers and topic authors is allowed unless the Topic Author is responding to a question submitted during the Pre-release period. DoD On-line Q&A System: After the pre-release period, until May 20, 2026, at 12:00 PM ET, proposers may submit written questions through the DoW On-line Topic Q&A at https://www.dodsbirsttr.mil/submissions/login/ by logging in and following instructions. In the Topic Q&A system, the questioner and respondent remain anonymous but all questions and answers are posted for general viewing. DoW Topics Search Tool: Visit the DoW Topic Search Tool at www.dodsbirsttr.mil/topics-app/ to find topics by keyword across all DoW Components participating in this BAA.
|