OUSD (R&E) CRITICAL TECHNOLOGY AREA(S): Human-Machine Interfaces; Space Technology; Trusted AI and Autonomy OBJECTIVE: Develop a next-generation wearable neural and/or physiological interface and corresponding algorithms, hardware, and software that provide a real-time/semi-real time link between a human being and a secondary technology (e.g. augmented reality, intervention) in a manner that augments Air Force-relevant cognitive performance (e.g. training-related, decision making) in able-bodied nonclinical populations. DESCRIPTION: Mechanisms to enhance cognitive performance are important for future success (AF 2030 Strategy). Over the years, researchers have successfully used technology to enhance cognitive performance (Cinel et al., 2019). These cognitive augmentation technologies are often dependent on the underlying cognitive state and unique biological profile of the individual at the time of use. However, most commercial cognitive augmentation technologies do not take into account the cognitive state of the individual and instead deliver augmentation under fixed predetermined schedules and/or protocols (i.e. open-loop augmentation). Neural interfaces (e.g. brain machine/computer interfaces) can serve as a real-time bridge between an individual's cognitive state and cognitive augmentation technologies (Chaudhary et al., 2016; Miranda et al., 2014). Researchers have shown that these brain-in-loop augmentations outperform open-loop augmentation (Basu et al., 2021; DeBettencourt et al., 2015; Raphael et al., 2009; Zrenner et al., 2016). These systems, however, often do not have the necessary spatial and/or temporal resolution, usability, and/or algorithm maturity to be useful for Air Force applications (e.g. personalized training, cognitive interventions). Further, most cognitive augmentation technologies either emphasize the sensing element (e.g. electroencephalography, functional near infrared spectroscopy, eye tracking, behavioral measures), the software to interpret these signals (e.g. signal processing, machine learning algorithm library) or the cognitive augmentation technology elements (e.g. augmented reality system, neuromoduation device, artificial intelligence-inspired training software, advanced visualizations, external stimuli) and fail to link these elements to quantifiable cognitive performance measures (e.g. accelerated learning, improved working memory, reduced reaction time and accuracy). Therefore, this SBIR seeks to develop a system that integrates the physiological and behavioral/biological sensing, software, and augmentation technology elements into an easy to setup usable form factor that is designed for use outside of laboratory. The developed system must also have a demonstrable positive impact on cognitive performance. PHASE I: This topic is only soliciting Direct to Phase II (D2P2) level proposals. Proposers must provide data demonstrating the appropriate function of an existing neural interface prototype, sensing elements, and preliminary software for processing neural interface prototype outputs. While proposers are not required to have the cognitive augmentation technology already integrated into the neural interface, they should have identified the cognitive augmentation technology that will be integrated in phase II. Additionally, the existing neural interface prototype must either already have the relevant software and hardware input/output architecture in place, which will form the basis for integration between the neural interface and augmentation technology, or provide sufficient documentation to substantiate a path towards this architecture within the period of performance of Phase II. PHASE II: Performers will need to demonstrate: 1) the performance enhancement benefit and 2) the usability of a wearable neural interface combined with augmentation technology. The performance enhancement benefit will depend on the quality (e.g. signal to noise ratio, number of sensors) of physiological and behavioral signals (e.g. brain, eye tracking, behavioral) extracted from the human being, the ability of algorithms to extract useful information from these signals (e.g. bits of entropy/mutual information, algorithm goodness-of-fit or classification accuracy, receive operator characteristic curve performance) the role the cognitive augmentation technology(ies) has on performance, and the interaction between algorithm outputs and the secondary technology/intervention. The performance enhancement benefit must be Air Force relevant (e.g. improve learning, decision making) and target able-bodied nonclinical populations. The cognitive performance enhancement benefit of the combined neural interface and cognitive augmentation technology system components must outperform what each system component contributes to cognitive performance individually. The usability of the neural interface will depend on comfort levels derived from wearing the device, robustness to motion artifacts, portability, and ease and duration to setup and cleanup. Schedule/Milestones/Deliverables: Month 1: Report on product development project plan that adapts existing technology as much as possible or develops a new platform if necessary. IRB and HRPO approvals or data collection effort enrollment when approvals obtained. Month 3: Report on: Progress toward month 6 goals; IRB and HRPO approvals or data collection effort enrollment when approvals obtained. Month 6: Report on: Month 6 demonstration; IRB and HRPO approvals or data collection effort enrollment when approvals obtained. Month 9: Report on: Progress toward month 12 goals; IRB and HRPO approvals or data collection effort enrollment when approvals obtained. Month 12: Report on: Month 12 demonstration; IRB and HRPO approvals or data collection effort enrollment when approvals obtained. Month 15: Report on: Progress toward month 18 goals; IRB and HRPO approvals or data collection effort enrollment when approvals obtained. Month 18: Report on: Month 18 demonstration; Performers must show performance enhancement benefit using prototype neural interface; IRB and HRPO approvals or data collection effort enrollment when approvals obtained. Month 21: Report on: Progress towards month 24 goals enrollment; IRB and HRPO approvals or data collection effort enrollment when approvals obtained. Month 24: Report on: Month 24 demonstration: Performers must show usability and performance benefit in finalized form factor. PHASE III DUAL USE APPLICATIONS: While this SBIR application focuses on a capability benefit designed to enhance cognitive performance in able-bodied nonclinical populations, a similar benefit might translate to non-cognitive performance domains within able-bodied nonclinical populations. Similarly, cognitive and/or non-cognitive benefits seen in able-bodied nonclinical populations could translate to clinical populations. REFERENCES: 1. Basu, I., Yousefi, A., Crocker, B., Zelmann, R., Paulk, A. C., Peled, N., Ellard, K. K., Weisholtz, D. S., Cosgrove, G. R., Deckersbach, T., Eden, U. T., Eskandar, E. N., Dougherty, D. D., Cash, S. S., & Widge, A. S. (2021). Closed-loop enhancement and neural decoding of cognitive control in humans. Nature Biomedical Engineering. https://doi.org/10.1038/s41551-021-00804-y 2. Chaudhary, U., Birbaumer, N., & Ramos-Murguialday, A. (2016). Brain-computer interfaces for communication and rehabilitation. Nature Reviews Neurology, 12(9), 513 525. https://doi.org/10.1038/nrneurol.2016.113 3. Cinel, C., Valeriani, D., & Poli, R. (2019). Neurotechnologies for human cognitive augmentation: Current state of the art and future prospects. Frontiers in Human Neuroscience, 13(January). https://doi.org/10.3389/fnhum.2019.00013 4. DeBettencourt, M. T., Cohen, J. D., Lee, R. F., Norman, K. A., & Turk-Browne, N. B. (2015). Closed-loop training of attention with real-time brain imaging. Nature Neuroscience, 18(3), 470 478. https://doi.org/10.1038/nn.3940 5. Miranda, R. A., Casebeer, W. D., Hein, A. M., Judy, J. W., Krotkov, E. P., Laabs, T. L., Manzo, J. E., Pankratz, K. G., Pratt, G. A., Sanchez, J. C., Weber, D. J., Wheeler, T. L., & Ling, G. S. F. (2014). DARPA-funded efforts in the development of novel brain-computer interface technologies. Journal of Neuroscience Methods, 244, 52 67. https://doi.org/10.1016/j.jneumeth.2014.07.019 6. Raphael, G., Berka, C., Popovic, D., Chung, G. K. W. K., Nagashima, S. O., Behneman, A., Davis, G., & Johnson, R. (2009). I-NET : Interactive neuro-educational technology to accelerate skill learning. Proceedings of the 31st Annual International Conference of the IEEE Engineering in Medicine and Biology Society: Engineering the Future of Biomedicine, EMBC 2009, September, 4803 4807. https://doi.org/10.1109/IEMBS.2009.5332638 7. Zrenner, C., Belardinelli, P., M ller-Dahlhaus, F., & Ziemann, U. (2016). Closed-Loop Neuroscience and Non-Invasive Brain Stimulation: A Tale of Two Loops. Frontiers in Cellular Neuroscience, 10(April), 1 7. https://doi.org/10.3389/fncel.2016.00092 KEYWORDS: Brain Machine Interface; Brain Computer Interface; Training, Learning; Cognitive Enhancement; Closed loop systems; Extended Reality; Neuromodulation; Cognitive Interventions; Cognitive State
