Brain injury can alter an individual’s life trajectory in profound ways. Quite frequently, difficulties are related to deficits in directing multiple aspects of cognition and emotion to optimally serve a current goal, especially in the setting of challenges. Many post-9/11 Veterans who have experienced a brain injury may benefit from learning skills to better regulate their underlying cognitive-emotional state while working toward their personal goals. However, learning how to apply state regulation skills (SRS) strategically and flexibly when they are most needed in everyday life is challenging. In order to address this issue, we previously designed and developed digital scenarios to assist with skill learning; scenarios function as systematic learning opportunities to practice SRS in a wide range of cognitively challenging contexts while also providing individualized data-driven feedback to guide the learning process. In this new project, we seek to further strengthen learning of SRS by utilizing physiological data reflective of underlying brain state as individualized biofeedback during training scenarios. Providing learners (Veterans) access to aspects of their underlying brain state that are otherwise inaccessible (e.g., brain network activity) can empower Veterans to learn skills for self-regulation. Taken together, intensive practice with applying SRS to multiple and varied cognitive challenges accompanied by biofeedback has the potential to accelerate and strengthen effective skill learning. Post 9-11 Veterans with a history of TBI (age 21-60) will be recruited for participation in this project. Participants will perform a series of computerized cognitive challenge tasks while having aspects of their central and peripheral neurophysiology monitored. The preliminary objective of these investigations is to identify potentially useful training targets for biofeedback by exploring biometric variables that index changes in brain state with cognitive challenges as well as application of SRS. Based upon these results, pilot feedback models we will designed, developed, and integrated into digital scenarios. Participants will contribute to the design and implementation process by providing user-feedback on factors such as salience of feedback, ease-of-use, and ability to utilize feedback to moderate the training target. The knowledge gained from this project will help inform the development of a closed-loop system for skill training following brain injury. In this system, multiple reinforcing layers of feedback (behavioral performance, data on skill use, neurophysiology, and self-report) will converge to help Veterans learn how to best use these skills to improve their everyday functioning. Such a system also will be a valuable tool to assist with local and remote rehabilitation.