CAREER: MINDWATCH: Multimodal Intelligent Noninvasive brain state Decoder for Wearable AdapTive Closed-loop arcHitectures

类似智能手表的可穿戴设备已经实现了对生命体征和身体活动的无缝跟踪，但仍缺乏一个重要特征：它们目前无法提供有关大脑状态的任何信息，也无法调节大脑功能以优化人类健康和表现。该项目旨在使可穿戴设备具备这些功能成为可能。了解大脑的状态不仅在临床研究中非常有价值，而且对提高人类在各种日常生活活动中的表现也至关重要。虽然直接从头皮区域记录神经信号是可能的，但在日常生活中使用是不切实际的。为了填补这一空白，这个项目的目标是开创一种闭环大脑感知可穿戴架构，称为MINDWATCH。这使得(1)从非侵入性可穿戴设备解码多维大脑状态和(2)应用纠正控制成为可能。MINDWATCH将改变医疗保健服务（例如老龄化、自闭症、痴呆症）以及人类绩效和生产力的提高（例如在线学习、智能工作场所）。例如，了解心理健康和认知参与可以检测学生是否抑郁或没有认知参与/学习，从而有可能及早采取纠正措施。这项研究与教育和外联活动相结合，重点是增加少数民族对科学和工程的参与。这些活动包括举办STEM K12实践活动，监督本科生研究实习生和顶级高级设计项目，制作教育视频以及跨学科课程开发。该项目旨在克服实现大脑感知可穿戴设备的障碍，通过开创一种变革性的系统理论计算工具集，用于无创闭环可穿戴架构，该架构可以在不需要神经记录的情况下监测和调节大脑功能。所提出的框架将(1)在现实环境中推断离散的大脑相关事件，(2)基于推断的大脑活动解码多维潜在神经行为状态，以及(3)应用鲁棒自适应控制将神经行为状态维持在期望的范围内。闭环框架将通过人机交互环境和心理健康实验得到严格验证。该研究将为多时间尺度上的多模态二元和连续生理观测分析提供基础的统计信号处理和控制理论工具。虽然最初的重点是大脑功能的两个方面，即心理健康和认知参与，但提出的框架为研究计算神经科学中更广泛的问题提供了机会。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Smartwatch-like wearables have enabled seamless tracking of vital signs and physical activities, but still lack a significant feature: they are currently unable to provide any information about brain states or to modulate brain function for optimizing human health and performance. This project aims to make it possible for wearables to feature such capabilities. Being aware of brain states is not only extremely valuable in clinical studies but is also crucial to improving human performance in various everyday life activities. While recording neural signals directly from the scalp region is possible, it is impractical for use in everyday life. In order to fill this gap, the goal of this project is to pioneer a closed-loop brain-aware wearable architecture called MINDWATCH. This enables (1) decoding multidimensional brain states from noninvasive wearable devices and (2) applying corrective control. MINDWATCH will transform healthcare delivery (e.g., aging, autism, dementia) as well as human performance and productivity enhancement (e.g., online learning, smart workplaces). For instance, knowledge of mental health and cognitive engagement can enable detecting if a student is depressed or is not cognitively engaged/learning, which makes it possible to take corrective action early on. The research is integrated with educational and outreach activities with an emphasis on increasing the participation of minorities in science and engineering. These activities include hosting hands-on STEM K12 events, supervising undergraduate research interns and capstone senior design projects, creating educational videos, and interdisciplinary course development.This project seeks to overcome the barriers to achieving brain-aware wearables by pioneering a transformative system-theoretic computational toolset for noninvasive closed-loop wearable architectures that monitor and modulate brain function without needing neural recordings. The proposed framework will (1) infer discrete brain-related events in real-world settings, (2) decode multidimensional latent neurobehavioral states based on inferred brain activity, and (3) apply robust adaptive control to maintain the neurobehavioral states within desired ranges. The closed-loop framework will be rigorously validated using experiments on interactive human-technology environments and mental health. The proposed research will provide foundational statistical signal processing and control-theoretic tools for the analysis of multimodal binary and continuous physiological observations that occur on multiple time-scales. While the initial focus is on two aspects of brain function, namely mental health and cognitive engagement, the proposed framework opens up the opportunity to investigate broader questions in computational neuroscience.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.