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

脑机接口系统有可能帮助和显著改善患有神经疾病的人的生活，如癫痫、帕金森病、肌萎缩侧索硬化症以及脑和脊髓损伤。在全美，估计有1亿人在与这种神经疾病作斗争。该项目旨在使脑机接口发生革命性变化，使其在日常使用中更容易获得和实用。通过开发新的节能技术，该项目承诺帮助运动残疾或感觉障碍的个人与他们的环境无缝互动。脑机接口的这些进步不仅将提高用户的机动性和独立性，还将降低与当前系统相关的成本和技术挑战。除了对科学和技术的贡献外，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，包括那些来自代表性不足的群体和残疾学生，从事康复工程方面的开创性研究。与教育和关注残疾问题的组织的合作强调了它致力于培养未来的科学家和工程师，从而丰富该领域并造福整个社会。该项目旨在显著推进脑机接口(BMI)技术，重点是为运动残疾或感觉障碍的个人创建双向系统。主要目标是开发高移动性、高能效和高性价比的BMI解决方案，超越当前基于实验室的系统的限制。该方法包括设计基于混合信号尖峰神经网络(SNN)的算法，以利用模拟和数字电路元件的优势进行高效的神经解码。这一新方法有望显著提高BMI硬件的能效。此外，该项目还将探索集成电路的开发，以实现BMI的小型化和降低成本，使其更容易获得和更实用于日常使用。另一个关键的焦点是促进芯片上学习和提取稳定潜变量的算法和电路，旨在提高神经解码器在皮质内BMI固有变异性下的稳健性和适应性。最终，将这些技术集成到单个片上系统(SoC)中并使用动物模型进行验证将标志着该领域向前迈出的重要一步。该项目有可能为BMI技术做出开创性的贡献，改善残疾人的生活质量，促进康复工程的发展。该奖项反映了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。

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.