DDDAS-TMRP: Dynamic Data-Driven Brain-Machine Interfaces

本项目中考虑的两个相关DDDAS应用领域是（1）通过活体受试者实验进行认知大脑建模，以及（2）设计大脑启发的辅助系统，通过脑机接口（BMI）帮助具有严重运动行为限制的人类（例如截瘫患者）。简单地说，BMI使用大脑信号直接控制计算机和机器人等设备。今天的BMI设计非常原始，与动物大脑和身体之间的无缝接口相去甚远。在一个健康的动物，大脑不断学习和适应新的身体运动的需要，除了提供完美的定时信号的运动系统。在这个过程中，大脑接收并使用感觉反馈来学习和产生导致有目的运动的信号。为了更接近于对人类有用的BMI设计，需要更好的大脑运动控制和运动规划模型，以及沿着必要的自适应算法和计算架构，这些算法和计算架构需要真实的时间执行。鉴于这些目标，该项目的活动旨在通过开发动态数据驱动的BMI（DDDBMIs）的模型，算法和计算架构来显着推进BMI研究的最新技术水平，这些模型，算法和计算架构实现了最近提出的先进的运动控制大脑模型。在所提出的方法中实现这一目标也将允许解决当前BMI研究中的一个主要问题：截瘫患者不能训练自己的网络模型，因为他们不能移动他们的肢体。控制接口架构是分布式的，并借鉴了最近的运动神经生理学模型，这将使更好的整体性能。学习同时发生的主题和控制模型的协同方式，这需要更强大的适应计划。选择性地使用许多计算模型是为什么需要动态数据驱动系统来提供DDDBMI的计算需求的原因。该项目的跨学科活动紧密交织在一起，围绕DDDBMI组件的开发和整合到BMI研究平台。对中间件的研究解决了具有服务质量保证的网格资源动态聚合的需求，并支持动态计算转向。自适应算法的研究集中在新的数据模型和学习算法。对大脑建模的研究集中在运动控制的认知模型和推进我们对运动神经生物学的理解。从长远来看，BMI实验研究平台将具有双重作用：它将有助于验证正在研究的大脑模型，并将为如何设计供截瘫患者使用的BMI提供见解。

Two related DDDAS application areas considered in this project are (1) cognitive brain modeling from experiments with live subjects and (2) the design of brain-inspired assistive systems to help human beings with severe motor behavior limitations (e.g. paraplegics) through brain-machine interfaces (BMIs). Simply stated, a BMI uses brain signals to directly control devices such as computers and robots. Today's BMI designs are extremely primitive and are a far cry from the seamless interface between brain and body in animals. In a healthy animal, the brain constantly learns and adapts to the needs of new physical movement, in addition to providing perfectly timed signals to the motor system. In this process, the brain receives and uses sensory feedback to both learn and generate the signals that lead to purposeful motion. In order to inch closer to BMI designs that are of use to humans, better models of brain motor control and movement planning are needed along with the necessary adaptive algorithms and computational architecture needed for their execution in real time. In light of such goals, this project's activities aim to significantly advance the state of the art of BMI research by developing the models, algorithms and computational architecture of dynamically-data-driven BMIs (DDDBMIs) that implement recently proposed advanced brain models of motor control. Achieving this goal in the proposed approaches will also allow to address a chief problem in current BMI research: The fact that paraplegics cannot train their own network models because they cannot move their limbs.The research on DDDBMI systems conducted under this project is a drastic departure of the conventional BMI paradigm. The control interface architecture is distributed and borrowed from recent models of neurophysiology of movement, which will enable better overall performance. Learning occurs simultaneously for the subject and the control models in a synergistic manner, which requires more powerful adaptation schemes. Selective use of many computational models is the reason why a dynamically data-driven system is needed to provide the computational needs of a DDDBMI. The project interdisciplinary activities are closely intertwined around the development and integration of the DDDBMI components into a platform for BMI research. Research on middleware addresses the need for dynamic aggregation of Grid-resources with Quality-of-Service guarantees, and support for dynamic computation steering. Research on adaptive algorithms focuses on new data models and learning algorithms. Research on brain modeling concentrates on cognitive models of motor control and advancing our understanding of the neurobiology of movement. In the long run, the BMI experimental research platform will have a dual role: it will help validate the brain models under investigation and it will provide insights on to how to design BMIs for use by paraplegic patients.