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 大脑皮质内脑机接口(IBCI)是一种很有前途的解决方案，可用于恢复患有脑病的人的功能 瘫痪，其性能比非侵入性类似物高出几个数量级。当前iBCI监视器 用户的大脑信号，并使用解码算法将大脑的测量结果直接映射到 外部变量，如计算机光标速度。四肢瘫痪患者表示，恢复手 功能是它们的最高优先级，然而，当前手持的iBCI无法与 在独立控制的手指的数量、控制的质量和 同时使用手指和手臂的运动。这些局限性阻碍了临床的广泛应用 部署手持iBCI。 最近的研究表明，运动皮质的许多活动并不直接对应于 运动变量(如手指角度)，而是充当内部的计算角色，以可靠地生成 马达输出。神经种群动力学，这是支配神经种群活动进化的规则 随着时间的推移，可以用来更准确地解析运动皮质中与运动和计算相关的活动。 基于动力学的解码器首先对驱动记录的神经活动的动力学进行建模，然后使用解码器来映射 运动的估计动力学。基于动力学的解码已经提高了IBCI的性能 预测猴子手臂运动的准确率为36%，但基于动力学的解码效果如何仍不得而知 可以预测人类手指的运动。 这项建议的目的是用IBCI恢复瘫痪患者的灵巧手指控制。 中心假设是，基于动力学的解码器将弥合手工聚焦的解码器在能力上的差距 IBCI和健全的手功能。拟议研究的理由是，性能 基于动力学的解码器引入的改进将从预测手臂运动转变为 预测手指的运动。这一假说将在上肢瘫痪患者身上通过 以下两个具体目标：1)增加机械手的独立控制手指的数量 在不牺牲控制质量的情况下，以及2)保持控制灵巧手指运动的性能 同时控制整个机械臂的运动。基于动力学的解码器将使用 基于最先进的人工神经网络(ANS)的动力学模型，以实现对 底层动态与基于神经网络的动态解码器配对，以将估计的动态转换为 有动静。基于动力学的解码器将与传统的直接解码器进行比较 用于人类的iBCI。这项工作可能是为瘫痪患者提供通用的第一步 IBCI控制的机械臂，帮助他们独立完成家中的日常生活活动。