Long-term Robustness of Brain-Machine Interfaces

脑机接口的长期鲁棒性

• 批准号: 2598249
• 金额: --
• 依托单位: University of Cambridge
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2021
• 资助国家: 英国
• 起止时间: 2021 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2598249
• 关键词: Long; term; Robustness; Brain; Machine

This research project deals with Brain-Machine Interfaces (BMIs), devices that use measurements of a subject's brain to provide a mechanism for them to interact with a computer. One of the main obstacles to the viability of current BMI approaches lies in the changing nature of the operating environment: the neural patterns associated with a desired behaviour can manifest differently under new recording conditions, or may even fundamentally vary over time due to reconfiguration of the neurons involved. These issues are prevalent across various BMI designs, even though the operating principles of those designs rely on different kinds of measurements or signal processing. This project seeks to tackle this obstacle and improve the long-term robustness of BMIs through a variety of approaches: by developing novel digital signal processing techniques that better adapt to the variable operating environment, by improving our understanding of the underlying mechanics of neural variability to enable adaptive decoders to continuously calibrate themselves, and by investigating the nature of the feedback systems the brain itself requires to internally correct for this variability.The project is inherently cross-disciplinary, and touches upon areas of Neuroscience and Biological Informatics, Control Engineering, Digital Signal Processing, and Machine Learning.

该研究项目涉及脑机接口（BMI），即使用受试者大脑测量的设备，为他们提供与计算机交互的机制。当前BMI方法可行性的主要障碍之一在于操作环境的变化性质：与所需行为相关的神经模式在新的记录条件下可能表现不同，或者甚至可能由于所涉及的神经元的重新配置而随着时间的推移而发生根本性变化。这些问题在各种BMI设计中普遍存在，即使这些设计的工作原理依赖于不同类型的测量或信号处理。该项目旨在解决这一障碍，并通过各种方法提高BMI的长期稳健性：通过开发更好地适应可变操作环境的新颖数字信号处理技术，通过提高我们对神经可变性的潜在机制的理解以使自适应解码器能够连续地校准自身，通过研究反馈系统的性质，大脑本身需要在内部纠正这种变化。该项目本质上是跨学科的，涉及神经科学和生物信息学，控制工程，数字信号处理，和机器学习