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.

项目概要/摘要 皮质内脑机接口 (iBCIs) 是一种很有前途的解决方案，可以帮助患有此病的人恢复功能 与现有的 iBCIs 监视器相比，其性能提高了几个数量级。 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 四肢瘫痪的人表示可以恢复手部。 功能是他们的最高优先级，然而，当前以手动为中心的 iBCI 无法与 原生人手在独立控制的手指数量、控制质量以及 同时使用手指和手臂的运动这些限制阻碍了临床的广泛应用。 部署以手动为中心的 iBCIs。 最近的研究表明，运动皮层的大部分活动并不直接对应于 运动变量（如手指角度），而是充当内部计算角色来可靠地生成 运动输出。神经群体动态，这是控制神经群体活动进化的规则。 随着时间的推移，可用于更准确地解析运动皮层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 基于动态的解码估计已经提高了 iBCI 性能。 预测猴子手臂运动的准确率达 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动作。 该提案的目的是通过 iBCI 帮助瘫痪患者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动解码器和手动解码器之间的能力差距。 iBCIs 和健全的手部功能 本研究的基本原理是性能。 基于动力学的解码器引入的改进将从预测手臂运动转化为 预测手指运动的假设将通过上肢瘫痪的人进行测试。 以下两个具体目标：1）增加机械手独立控制的手指数量 在不牺牲控制质量的情况下，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将使用基于动力学的解码器。 最先进的基于人工神经网络（ANN）的动力学模型，以实现对 底层动力学与基于 ANN 的动力学解码器相结合，将估计的动力学转化为 基于动态的解码器将与传统的直接解码器进行比较。 这项工作可能是为瘫痪患者提供通用用途的第一步。 iBCI控制的机械臂帮助他们独立完成家里的日常生活活动。

项目成果

BRAND: A platform for closed-loop experiments with deep network models.

BRAND：深度网络模型闭环实验平台。

• 发表时间: 2023-08-12
• 作者: Ali, Yahia H;Bodkin, Kevin;Rigotti;Patel, Kushant;Card, Nicholas S;Bhaduri, Bareesh;Nason;Mifsud, Domenick M;Hou, Xianda;Nicolas, Claire;Allcroft, Shane;Hochberg, Leigh R;Yong, Nicholas Au;Stavisky, Serg
• 通讯作者: Stavisky, Serg