CAREER: Exploring Mixed-Signal Computation for Energy-Efficient and Robust Brain-Machine Interfaces

职业：探索节能且鲁棒的脑机接口的混合信号计算

• 批准号: 2338159
• 负责人: Sahil Shah
• 金额: $ 59.95万
• 依托单位: University of Maryland, College Park
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2024
• 资助国家: 美国
• 起止时间: 2024-03-01 至 2029-02-28
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2338159&HistoricalAwards=false
• 关键词: CAREER; Exploring; Mixed; Signal; Computation

Brain-Machine Interfaces systems have the potential to help and substantially improve lives of people that are affected by neurological disorders such as Epilepsy, Parkinson's Disease, Amyotrophic Lateral Sclerosis, as well as Brain and Spinal Cord Injuries. Across the United States, an estimated 100 million individuals grapple with such neurological disorders. This project aims to revolutionize Brain-Machine Interfaces making them more accessible and practical for everyday use. By developing new, energy-efficient technologies, the project promises to help individuals with motor disabilities or sensory impairments interact seamlessly with their environment. These advancements in Brain-Machine Interfaces will not only enhance mobility and independence for users but also reduce the costs and technical challenges associated with current systems. Beyond its scientific and technological contributions, the project is deeply committed to education and diversity. It offers unique opportunities for students, including those from underrepresented groups and with disabilities, to engage in pioneering research in rehabilitation engineering. Collaborations with educational and disability-focused organizations underscore its dedication to nurturing future scientists and engineers, thereby enriching the field and benefiting society at large. This project aims to significantly advance Brain-Machine Interface (BMI) technology, with a focus on creating bidirectional systems for individuals with motor disabilities or sensory impairments. The primary goal is to develop BMI solutions that are highly mobile, energy-efficient, and cost-effective, moving beyond the constraints of current laboratory-based systems. The approach includes the design of mixed-signal Spiking Neural Network (SNN)-based algorithms for efficient neural decoding, leveraging the strengths of both analog and digital circuit elements. This novel approach is anticipated to enhance the energy efficiency of BMI hardware substantially. Additionally, the project will explore the development of integrated circuits to miniaturize and reduce the cost of BMIs, making them more accessible and practical for everyday use. Another key focus is on algorithms and circuits that facilitate on-chip learning and extraction of stable latent variables, aiming to improve the robustness and adaptability of neural decoders under the inherent variability of intracortical BMIs. Ultimately, the integration of these technologies into a single System-on-Chip (SoC) and their validation using animal models will mark a significant step forward in the field. The project holds the potential for groundbreaking contributions to BMI technology, improving the quality of life for those with disabilities and advancing the state of rehabilitation engineering.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.

脑机界面系统有可能帮助并实质上改善受神经系统疾病影响的人的生活，例如癫痫，帕金森氏病，肌萎缩性侧向硬化症以及脑和脊髓损伤。在整个美国，估计有1亿个人应对这种神经系统疾病。该项目旨在彻底改变脑机界面，使它们更容易访问和实用。 通过开发新的节能技术，该项目有望帮助患有运动障碍或感觉障碍的人与环境无缝相互作用。脑机界面中的这些进步不仅会增强用户的移动性和独立性，而且还会降低与当前系统相关的成本和技术挑战。除了其科学和技术贡献外，该项目还致力于教育和多样性。它为学生提供了独特的机会，包括来自代表性不足的群体和残疾人，从事康复工程研究的开创性研究。与以教育和残疾为中心的组织的合作强调了其致力于培养未来的科学家和工程师的奉献精神，从而丰富了整个领域并为整个社会受益。 该项目旨在显着推进脑机界面（BMI）技术，重点是为运动障碍或感觉障碍的个体创建双向系统。主要目标是开发高度流动性，节能且具有成本效益的BMI解决方案超出了当前基于实验室的系统的限制。该方法包括设计混合信号尖峰神经网络（SNN）的算法，用于有效的神经解码，利用模拟和数字电路元件的优势。预计这种新颖的方法可以大大提高BMI硬件的能源效率。此外，该项目将探索综合电路的开发，以小型化和降低BMI的成本，从而使它们更容易访问和实用。另一个重点是算法和电路，促进稳定的潜在变量的片上学习和提取，旨在提高神经解码器在心脏内BMI的固有变异性下的鲁棒性和适应性。最终，将这些技术集成到单个芯片上（SOC）及其使用动物模型的验证将标志着该领域的重要一步。该项目具有对BMI技术的开创性贡献的潜力，改善了残疾人的生活质量并推进了康复工程的状况。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛影响的审查标准通过评估来获得支持的。