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

项目总结/摘要 皮质内脑机接口（iBCIs）是恢复功能的人有前途的解决方案， 瘫痪的数量级更大的性能比他们的非侵入性类似物。显示iBCI监视器 用户的大脑发出信号，并使用解码算法将来自大脑的测量结果直接映射到 外部变量，如计算机光标速度。四肢瘫痪的人表示， 功能是它们的最高优先级，然而，当前以手动为重点的iBCI无法与 在独立控制的手指的数量、控制的质量和 手指与手臂的运动同时使用。这些限制阻碍了广泛的临床 部署以手动为中心的iBCI。 最近的研究表明，运动皮层的许多活动并不直接对应于 运动变量（如手指角度），而是提供一个内部的，计算的作用，以可靠地生成 电机输出。神经群体动力学，这是控制神经群体活动进化的规则 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，然后使用解码器映射 运动的估计动态。基于动态的解码已经提高了iBCI的性能， 预测猴子手臂运动的准确率为36%，但目前尚不清楚基于动力学的解码 可以预测人类手指的运动。 该提案的目的是用iBCI在瘫痪患者中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聚焦和手动聚焦之间的能力上的差距。 iBCI和健全的手功能。拟议研究的基本原理是， 基于动态的解码器所带来的改进将从预测手臂运动转变为 预测手指运动。这一假设将在上肢瘫痪的人中进行测试， 具体有两个目的：1）增加机器人手的独立控制手指的数量， 而不牺牲控制质量，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 国家的最先进的人工神经网络（ANN）为基础的动态模型，以实现最佳的估计， 基础动态与基于ANN的动态解码器配对，以将估计的动态转换为 运动基于动态的解码器将与传统上已经被解码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控制的机器人手臂，帮助他们在家中独立完成日常生活活动。

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.