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，但仍然缺乏一个重要功能：它们目前无法提供任何有关大脑状态的信息，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有此类功能成为可能。了解大脑状态不仅在临床研究中非常有价值，而且对于提高人类在各种日常生活活动中的表现也是至关重要的。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，该项目的目标是开创一种名为MINDWATCH的闭环式大脑感知可穿戴架构。这使得(1)从非侵入性可穿戴设备中解码多维大脑状态，以及(2)应用纠正性控制。MINDWATCH将改变医疗保健的提供(例如，老龄化、自闭症、痴呆症)以及人类表现和生产力的提高(例如，在线学习、智能工作场所)。例如，心理健康和认知投入的知识可以检测到学生是否抑郁或没有认知投入/学习，这使得在早期采取纠正行动成为可能。这项研究与教育和外联活动相结合，重点是增加少数群体对科学和工程的参与。这些活动包括主持实践STEM K12活动，指导本科研究实习生和顶尖高级设计项目，创建教育视频，以及跨学科课程开发。该项目寻求通过开创一种用于非侵入性闭环系统可穿戴体系结构的变革性系统理论计算工具集来克服实现大脑感知可穿戴设备的障碍，该可穿戴设备无需神经记录即可监控和调节大脑功能。该框架将(1)在真实环境中推断与脑相关的离散事件，(2)基于所推断的大脑活动来解码多维潜在神经行为状态，以及(3)应用稳健的自适应控制来将神经行为状态维持在期望的范围内。该闭环框架将通过在人机交互环境和心理健康方面的实验进行严格验证。这项拟议的研究将为分析发生在多个时间尺度上的多模式、二元和连续的生理观察提供基本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，即心理健康和认知投入，但拟议的框架为研究计算神经科学中更广泛的问题打开了机会。这一奖项反映了NSF的法定使命，并通过使用基金会的智力优势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.