I-Corps: Translation Potential of a Wearable, Brain-computer Interface System for In-home Stroke Rehabilitation

I-Corps项目更广泛的影响是开发一种新型中风康复系统，通过引入一种专为家庭使用的可穿戴电极系统来实现。中风是导致残疾的主要原因之一，给个人和卫生保健系统带来了巨大的经济负担。该项目的创新方法可以使中风幸存者能够在舒适的家中进行康复。这项技术不仅可以提高患者的治疗效果，还有助于解决与中风残疾相关的经济挑战。该项目还可能标志着技术和医疗保健交叉领域的重大进步，有可能改善中风康复的前景，使其更具包容性、便利性，并在更广泛的人群中具有经济可行性。这个I-Corps项目利用体验式学习和对行业生态系统的第一手调查来评估该技术的翻译潜力。该解决方案基于为家庭中风康复设计的可穿戴系统的开发，集成了嵌入基于脑电图（EEG）的脑机接口（BCI）的电极系统。这些组件包括一个具有耐用电极的可拉伸网状耳机，一个抗干扰的无线系统，以及促进无监督脑机接口操作的智能机器学习算法。通过解决与传统凝胶电极相关的挑战和专家监督的必要性，该技术代表了神经记录能力的新进步。脑机接口技术、无线通信和机器学习算法的融合不仅可以使中风康复更容易、更方便、技术更先进，而且还有望诊断和治疗各种神经系统疾病。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。

The broader impact of this I-Corps project is the development of a novel stroke rehabilitation system, achieved through the introduction of a wearable electrode system designed for in-home use. Stroke, a leading cause of disability, places substantial economic burdens on individuals and healthcare systems. The project's innovative approach could empower stroke survivors to engage in rehabilitation within the comfort of their homes. This technology could not only enhance patient outcomes but also help address the economic challenges associated with stroke disabilities. This project could also signify a critical advancement at the intersection of technology and healthcare, with the potential to improve the landscape of stroke rehabilitation by making it more inclusive, convenient, and economically viable for a broader segment of the population.This I-Corps project utilizes experiential learning coupled with a first-hand investigation of the industry ecosystem to assess the translation potential of the technology. The solution is based on the development of a wearable system designed for in-home stroke rehabilitation, integrating an electrode system embedded with an electroencephalography (EEG)-based brain-computer interfaces (BCI). The components include a stretchable mesh headset featuring durable electrodes, a wireless system resilient to interference, and intelligent machine learning algorithms facilitating unsupervised brain-computer interface operation. By addressing the challenges associated with conventional gel-based electrodes and the necessity for expert supervision, this technology represents a novel advancement in neural recording capabilities. The fusion of brain-computer interface technology, wireless communication, and machine learning algorithms could not only make stroke rehabilitation more accessible, convenient, and technologically advanced but also holds promise for diagnosing and treating a variety of neurological disorders.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.