The investigator plans to improve systems that rely on brain-computer interfaces that are used in virtual and augmented reality environments. These improvements will enhance comfort and reliability when used by individuals with disorders within the autism spectrum. The benefits of these improvements will advance the effectiveness of treatments for emotion regulation and behavioral interventions. There is a growing interest in complementing such behavioral clinical treatments with various low-cost and easy-to-access technology-based tools to improve therapy efficacy. However, it was shown that training through existing technology-based Autism Spectrum Disorder (ASD) intervention tools does not usually generalize to real-life activities for many reasons. The investigator will develop an intervention for ASD to reinforce emotion regulation strategies based on real-time monitoring and analysis. Specifically, the planned electroencephalography (EEG)-guided brain-computer interface (BCI) technology could be used to complement all clinical treatments that focus on emotion regulation to decrease clinician time spent with each patient. The novel scientific discoveries and engineering enhancements will have overreaching contributions to develop ASD intervention techniques for (i) decreased depression and anxiety; (ii) decreased problematic behaviors including aggression in social interactions; and (iii) decreased functional impairment across different settings including school, work, home and community. Research and education goals will include: (i) course development; (ii) inclusion of researchers from K-12 to graduate level in cutting-edge interdisciplinary research environment to promote STEM careers; and (iii) establishing new outreach activities to inform the broader public about the proposed research outcomes and the latest technological advancements in research for technology-based ASD intervention.
The research objective of this specific project is to introduce a framework that will enable EEG-guided closed-loop: (i) monitoring of the brain responses of individuals during technology-based ASD intervention, and (ii) control of the presentation of clinical treatment strategy cues for emotion regulation in individuals with ASD. In particular, for such human-computer interfaces: (1) the proposition of monitoring brain responses through EEG during ASD intervention is novel, and (2) formulating design principles for model-based optimal EEG-guided closed-loop clinical treatment strategy cue presentation is transformative. These propositions of real-time probabilistic analysis of EEG are unique, and present a potentially game-changing opportunity to advance the generalization effect of existing technology-based ASD intervention for emotion regulation. This project will also contribute to developing new machine learning algorithms and neuroscience methods to identify EEG features associated with emotion regulation to classify between distress and non-distress conditions, and to distinguish among different distress levels. The developed models will be based on a solid mathematical framework based on variational autoencoders and Bayesian optimal statistical inference, information theoretic measures of feature selection for efficient learning, and computationally efficient optimization of modular and submodular monotonic or non-monotonic functions. Optimization algorithms will provide computationally efficient solutions that generate(sub)optimal feature selection strategies with performance guarantees.
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.
