ABSTRACT

This project aims to develop innovative technology that can assist therapists in assessing and rehabilitating social fears during talk therapy with teens suffering from anxiety or depression. According to the World Health Organization, depression and anxiety are leading causes of disability worldwide, affecting approximately 25% of the population and costing the global economy $1 trillion per year. The standard approach to treating teen anxiety or depression involves many weekly sessions of one-on-one exposure therapy with a therapist. Exposure therapy gradually exposes teens to real-life social situations that trigger their fears while providing them with tools to manage and tolerate their distress. Up to 50% of teens do not respond to this treatment approach, placing them at high risk for chronic symptoms, suicidality, disability, and a significantly shorter life expectancy. Treatment failure occurs partially because it is hard to recreate real-life social challenges within therapy sessions, so teens are not able to practice facing their fears under the direct supervision of their therapists. There is currently no commercially available product specifically designed to assess, recreate, and rehabilitate social fears during exposure therapy. This project, led by a multidisciplinary team, aims to address these challenges by developing augmented reality (AR) technology that creates simulated real-life situations that evoke social fears within the therapy environment, and markers of social fear that will be used to provide objective and actionable feedback to teens and their therapists as they practice techniques during therapy sessions. To achieve these goals, the project will include partnerships with community experts and offer multidisciplinary training opportunities for K-12 to graduate level, with an emphasis on inclusion of trainees from marginalized communities.

The research objective of this project is to introduce a prototype for clinical application of an AR-guided, electroencephalogram (EEG)-based exposure technology for socially fearful teens. The proposed technology will: (i) use novel hardware integration and software development to seamlessly synchronize EEG data acquisition with AR presentation of social fear scenarios during real-life interpersonal situations; (ii) accurately and continuously detect whether the teen is exhibiting fearful vs. not fearful responses through EEG feature selection and Bayesian inference methods (considering both individualized responses and generalized responses across population); (iii) design novel machine learning methods to identify individualized EEG-based fear indices; (iv) monitor EEG-based fear indices in real time and adjust the AR social fear scenarios to increase the level of fear when necessary; and (v) identify individualized thresholds to detect when the user is in the ?exposure zone? and provide visual feedback to prompt the teen when to apply specific exposure techniques as prescribed by the therapist. The proposed system has the potential to provide a technology-driven paradigm for exposure therapy that meaningfully reflects the social challenges experienced by depressed and anxious teens. Bayesian optimal statistical inference will support the technology, providing mathematically-driven frameworks that enhance the accuracy of EEG recordings coupled with AR-based headtracking. Outcomes will be openly disseminated in peer-reviewed articles, outreach programs, and code/data repositories.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

Please report errors in award information by writing to: awardsearch@nsf.gov.