Restoring Dexterous Hand Function with Artificial Neural Network-Based Brain-Computer Interfaces

利用基于人工神经网络的脑机接口恢复灵巧手功能

• 批准号: 10680206
• 负责人: Samuel Ross Nason-Tomaszewski
• 金额: $ 6.91万
• 依托单位: EMORY UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-06-01 至 2026-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10680206
• 关键词: Activities of Daily Living; Address; Algorithms; Automobile Driving; Behavioral; Brain; Bypass; Clinical; Cognitive; Computers; Controlled Environment; Data; Devices; Evolution; Fingers; Goals; Hand; Hand functions; Home; Human; Implanted Electrodes; Injury; Intention; Maps; Measurement; Methods; Modeling; Monitor; Monkeys; Motor; Motor Cortex; Motor output; Movement; Network-based; Neurons; Outcome; Paralysed; Pattern; Performance; Persons; Population; Population Dynamics; Posture; Prosthesis; Quadriplegia; Quality Control; Quality of life; Research; Research Proposals; Robotics; Role; Signal Transduction; Site; System; Testing; Time; Translating; Upper Extremity; Variant; Work; analog; arm; arm movement; artificial neural network; brain based; brain computer interface; clinical translation; dexterity; falls; finger movement; functional electrical stimulation; functional restoration; hand rehabilitation; improved; kinematics; neural; neural model; novel; robot control

Project Summary/Abstract Intracortical brain-computer interfaces (iBCIs) are promising solutions for restoring function to people with paralysis with orders of magnitude greater performance than their non-invasive analogs. Present iBCIs monitor the user’s brain signals and use a decoding algorithm to map the measurements from the brain directly to external variables, such as computer cursor velocity. People with tetraplegia have indicated that restoring hand function is their highest priority, however, current hand-focused iBCIs are unable to match the capabilities of the native human hand in terms of the number of independently-controlled fingers, the quality of the control, and the simultaneous use of the fingers with movements of the arm. These limitations hinder the widespread clinical deployment of hand-focused iBCIs. Recent studies have shown that much of the activity in motor cortex does not directly correspond to movement variables (like finger angles), but instead serves an internal, computational role to reliably generate motor outputs. Neural population dynamics, which are rules that govern the evolution of neural population activity over time, can be used to more accurately parse movement- and computation-related activity in motor cortex. Dynamics-based decoders first model the dynamics driving recorded neural activity, then use a decoder to map the estimated dynamics to movement. Dynamics-based decoding has already improved iBCI performance for predicting the arm movements of monkeys by 36%, but it remains unknown how well dynamics-based decoding can predict the movements of human fingers. The objective of this proposal is to restore dexterous finger control with an iBCI in people with paralysis. The central hypothesis is that dynamics-based decoders will bridge the gap in capabilities between hand-focused iBCIs and able-bodied hand function. The rationale for the proposed research is that the performance improvements introduced by dynamics-based decoders will translate from predicting arm movements to predicting finger movements. The hypothesis will be tested with people with upper extremity paralysis through the following two specific aims: 1) increasing the number of independently-controlled fingers of a robotic hand without sacrificing control quality, and 2) maintaining performance of controlling dexterous finger movements while simultaneously controlling movements of the entire robotic arm. The dynamics-based decoders will use state-of-the-art artificial neural networks (ANNs)-based dynamics models to achieve the best estimate of the underlying dynamics paired with ANN-based dynamics decoders to translate the estimated dynamics into movement. The dynamics-based decoders will be compared against direct decoders that have been traditionally used in human iBCIs. This work may be the first step toward providing people with paralysis a general-purpose iBCI-controlled robotic arm to assist them with independently completing activities of daily living at home.

项目摘要/摘要 Intracortical brain-computer interfaces (iBCIs) are promised solutions for restoring function to people with 与非侵入性类似物相比，瘫痪的表现高。目前的IBCIS监视器 用户的大脑信号并使用解码算法将大脑的测量值直接映射到 外部变量，例如计算机光标速度。患有四边形的人表示恢复手 功能是他们的最高优先级，但是，当前以手动为中心的IBCI无法匹配 根据独立控制的手指的数量，控制质量和 同时使用手指的手指。这些限制阻碍了宽度临床 部署手动注重的IBCIS。 最近的研究表明，运动皮层中的许多活性都不直接与 运动变量（如手指角），而是发挥内部计算作用来可靠地产生 电动机输出。神经种群动态，这是控制神经元人群活动演变的规则 随着时间的流逝，可用于更准确地解析运动皮层的运动和计算相关活动。 基于动力学的解码器首先模拟动力学驱动记录的神经活动，然后使用解码器映射 估计运动的动力。基于动态的解码已经改善了IBCI的性能 将猴子的手臂运动预测36％，但尚不清楚基于动态的解码如何 可以预测人手指的运动。 该提议的目的是通过瘫痪者的IBCI恢复灵巧的手指控制。 中心假设是，基于动态的解码器将弥合手工注重的功能的差距 IBCIS和健全的手功能。拟议研究的理由是表现 基于动力学的解码器引入的改进将转化为从预测手臂运动到 预测手指运动。该假设将通过上肢瘫痪的人通过 以下两个具体目标：1）增加机器人手的独立控制手指的数量 不牺牲控制质量，以及2）保持敏感手指运动的性能 同时控制整个机器人臂的运动。基于动态的解码器将使用 最先进的人工神经网络（ANN）基于基于的动力学模型，以实现对 基础动力学与基于ANN的动力学解码器配对，将估计的动态转化为 移动。将基于动态的解码器与传统上的直接解码器进行比较 用于人类ibcis。这项工作可能是向人们提供瘫痪的第一步 IBCI控制的机器人手臂协助他们独立完成日常生活的活动。