EFRI-M3C: Robust Decoder-Compensator Architecture for Interactive Control of High-Speed and Loaded Movements

EFRI-M3C：用于高速和负载运动交互控制的稳健解码器补偿器架构

• 批准号: 1137237
• 负责人: Sridevi Sarma
• 金额: $ 200万
• 依托单位: Johns Hopkins University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-09-15 至 2017-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1137237&HistoricalAwards=false
• 关键词: EFRI; M3C; Robust; Decoder; Compensator

Objective: Brain-machine interactive control (BMIC) of prosthetic limbs for high speed and natural movements is a major challenge. The current BMIC paradigm employs a feedforward interface between the brain and prosthetic, referred to as the "decoder", whose success relies heavily on the ability of the brain to adapt appropriately utilizing visual feedback information in a "certain" environment. Such decoders are trained using data from healthy subjects but are implemented as interfaces for spinal cord patients. The motor cortical output of the healthy subject is substantially different from that of an injured patient, and decoders do not account for spurious signals generated in the cerebellum due to the loss of proprioceptive data. Thus, the key challenge is to design robust decoders for BMIC of the future that take into account both cerebellar and cortical contributions. Intellectual Merit: We propose a novel Robust Decoder-Compensator (RDC) architecture for interactive control of fast movements in the presence of uncertainty. The RDC is a feedback interconnection that 1) decodes cortical signals to produce actuator commands that reflect motor intent, 2) corrects for spurious cerebellar signals generated in the absence of proprioceptive feedback, and 3) makes robust the interconnection to account for mismatches between models and reality. Multi-site intracranial EEG recordings in motor areas obtained from epilepsy patients executing fast and loaded movements will facilitate system identification of cortical structures in healthy and in spinal cord patients. The cortical models and the RDC architecture will belong to a class of linear parameter varying systems, and the RDC will be synthesized to maintain performance over a wide range of movements and environments. Finally, we will implement the interactive system on patients with implanted electrodes.Broader Impact: We provide a unified framework aimed at understanding human motor control, which incorporates cerebellar and cortical models and builds a BMIC for fast and natural movements, allowing patients with spinal cord injuries and cerebellar ataxia to execute rapid natural trajectories. Powerful extensions of closed-loop system identification and robust control synthesis techniques for LPV systems will be developed. This program will also give students rare opportunities to address challenges at the interface between systems engineering and neurobiology.

目的：假肢的脑机交互控制(BMIC)是实现高速自然运动的一大挑战。目前的BMIC模式在大脑和假肢之间采用了一个前馈接口，称为“解码器”，它的成功在很大程度上依赖于大脑在“特定”环境中利用视觉反馈信息进行适当适应的能力。这些解码器使用来自健康受试者的数据进行训练，但作为脊髓患者的接口实现。健康受试者的运动皮质输出与受伤患者的运动皮质输出有很大不同，解码器不会考虑由于本体感觉数据丢失而在小脑产生的虚假信号。因此，关键的挑战是为未来的BMIC设计稳健的解码器，同时考虑到小脑和皮质的贡献。智能优点：我们提出了一种新的稳健解码-补偿器(RDC)结构，用于在存在不确定性的情况下对快速运动进行交互控制。RDC是一种反馈互连，它1)解码皮质信号以产生反映运动意图的致动器命令，2)纠正在没有本体感觉反馈的情况下产生的虚假小脑信号，以及3)使互连更稳健，以解决模型和现实之间的不匹配。癫痫患者进行快速负重运动时所获得的运动区多点脑电记录将有助于系统识别健康人和脊髓患者的皮质结构。大脑皮层模型和RDC结构将属于一类线性参数变化系统，RDC将被综合以在广泛的运动和环境中保持性能。最后，我们将在植入电极的患者身上实施互动系统。广泛影响：我们提供了一个旨在了解人类运动控制的统一框架，该框架结合了小脑和皮质模型，并构建了一个用于快速自然运动的BMIC，使脊髓损伤和小脑性共济失调患者能够执行快速的自然轨迹。对闭环系统辨识和LPV系统的鲁棒控制综合技术进行了强有力的扩展。本课程还将为学生提供难得的机会来应对系统工程和神经生物学之间的相互作用的挑战。