NCS FR - Elucidating the relationship between motor cortex neural firing rates and dextrous finger movement EMG for use in brain computer interfaces

NCS FR - 阐明运动皮层神经放电率与灵巧手指运动肌电图之间的关系，用于脑机接口

• 批准号: 1926576
• 负责人: Cynthia Chestek
• 金额: $ 227.64万
• 依托单位: Regents of the University of Michigan - Ann Arbor
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-01 至 2024-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1926576&HistoricalAwards=false
• 关键词: NCS; FR; Elucidating; relationship; between

Prosthetic hands controlled directly by the nervous system have been the subject of science fiction for decades, and could lead to dramatic quality of life improvements for people with upper limb amputations or paralysis. The brain is the only known controller capable of moving a 5-fingered robot with high precision to use a wide variety of tools and objects. While we know a lot about the control signals in the brain and movement of the fingers, we do not have a good idea of how one gives rise to the other. Here, we will generate an enormous dataset, which will be publicly distributed to students and other scientists everywhere. It will include many channels of brain activity simultaneously with muscle activity to help work out the transformation between the two, and attempt to replicate this control system using artificial neural networks. A strong demonstration of brain controlled prostheses could lead to human studies, and a clinical system that could impact the quality of life for hundreds of thousands of people with amputations or paralysis, as well generating insights for smarter robotic systems. Beyond the direct output of the research, brain machine interfaces have the capability to inspire a large number of students, including those from underrepresented groups, into careers in science and technology, by showing clearly to a young audience how this kind of education can help people.This project proposes to record data simultaneously from the brain, muscles, and kinematics of the primate hand during complex finger movements, in order to replicate this control system. Specifically, the objective of this particular application is to establish the first such dataset in a nonhuman primate, recording 200 channels from motor cortex, 12 channels of EMG from the muscles, and precise kinematics during the acquisition of finger targets from 4 different degrees of freedom. Our central hypothesis is that firing rates can be transformed to EMG using a single layer neural nonlinearity followed by a regularized linear regression, and then transformed into finger kinematics through non-linear but well-characterized anatomy. This differs from upper limb signals, which can appear to be linearly modulated by endpoint velocity regardless of posture. We will complete this project with three objectives. In Objective 1, we will establish a world-class surgical team to create a nonhuman primate animal model with simultaneous chronic brain recording and EMG recording. In Objective 2, we will develop an algorithmic approach to map motor cortex firing rates to EMG as well as kinematics, with both offline and online testing. In Objective 3, we will explore low power circuitry to extract this information in real time, at a power consumption that would be appropriate for an implantable medical device. The overall scientific philosophy of this project is that the brain provides an example of a low power neural network, which is relatively shallow between motor cortex and EMG, for controlling a complex soft robotic system. Uncovering this relationship will enable us to use this approach for brain machine interfaces for paralysis as well as guiding future human made robotic approaches.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

由神经系统直接控制的假手几十年来一直是科幻小说的主题，并可能导致上肢截肢或瘫痪的人的生活质量得到显着改善。大脑是唯一已知的控制器，能够高精度地移动五指机器人，以使用各种各样的工具和物体。虽然我们对大脑中的控制信号和手指的运动有很多了解，但我们并不知道一个信号是如何产生另一个信号的。在这里，我们将生成一个巨大的数据集，并将公开分发给世界各地的学生和其他科学家。它将包括大脑活动的许多通道，同时肌肉活动，以帮助解决两者之间的转换，并尝试使用人工神经网络复制这种控制系统。一个强有力的大脑控制假体的演示可能会导致人类研究，以及一个临床系统，可能会影响数十万截肢或瘫痪者的生活质量，并为更智能的机器人系统提供见解。除了研究的直接输出之外，脑机接口有能力激励大量学生，包括那些来自代表性不足的群体的学生，进入科学和技术领域，向年轻观众清楚地展示这种教育如何帮助人们。该项目提出在复杂的手指运动过程中同时记录来自大脑，肌肉和灵长类动物手的运动学数据，来复制这个控制系统。具体而言，该特定应用的目的是在非人类灵长类动物中建立第一个这样的数据集，记录来自运动皮层的200个通道，来自肌肉的EMG的12个通道，以及在从4个不同自由度采集手指目标期间的精确运动学。 我们的中心假设是，可以使用单层神经非线性，然后通过正则化线性回归将放电率转换为EMG，然后通过非线性但特征良好的解剖结构转换为手指运动学。 这与上肢信号不同，上肢信号可以看起来由端点速度线性调制，而不管姿势如何。我们将以三个目标完成这个项目。在目标一中，我们将建立一个世界一流的手术团队，建立一个非人灵长类动物模型，同时进行慢性脑记录和肌电图记录。在目标2中，我们将开发一种算法方法，将运动皮层放电率映射到EMG以及运动学，并进行离线和在线测试。在目标3中，我们将探索低功耗电路，以真实的时间提取此信息，功耗适合植入式医疗器械。这个项目的总体科学理念是，大脑提供了一个低功耗神经网络的例子，它在运动皮层和EMG之间相对较浅，用于控制复杂的软机器人系统。揭示这种关系将使我们能够使用这种方法的脑机接口瘫痪，以及指导未来的人类制造的机器人approaches.This奖项反映了NSF的法定使命，并已被认为是值得的支持，通过评估使用基金会的智力价值和更广泛的影响审查标准。

项目成果

The impact of task context on predicting finger movements in a brain-machine interface.

• DOI: 10.7554/elife.82598
• 发表时间: 2023-06-07
• 期刊: eLife
• 影响因子: 7.7
• 作者: Mender MJ;Nason-Tomaszewski SR;Temmar H;Costello JT;Wallace DM;Willsey MS;Ganesh Kumar N;Kung TA;Patil P;Chestek CA
• 通讯作者: Chestek CA

Restoring continuous finger function with temporarily paralyzed nonhuman primates using brain-machine interfaces.

使用脑机接口恢复暂时瘫痪的非人类灵长类动物的连续手指功能。

• DOI: 10.1088/1741-2552/accf36
• 发表时间: 2023
• 期刊: Journal of neural engineering
• 影响因子: 4
• 作者: Nason-Tomaszewski,SamuelR;Mender,MatthewJ;Kennedy,Eric;Lambrecht,JorisM;Kilgore,KevinL;Chiravuri,Srinivas;GaneshKumar,Nishant;Kung,TheodoreA;Willsey,MatthewS;Chestek,CynthiaA;Patil,ParagG
• 通讯作者: Patil,ParagG

Real-time linear prediction of simultaneous and independent movements of two finger groups using an intracortical brain-machine interface.

• DOI: 10.1016/j.neuron.2021.08.009
• 发表时间: 2021-10-06
• 期刊: Neuron
• 影响因子: 16.2
• 作者: Nason SR;Mender MJ;Vaskov AK;Willsey MS;Ganesh Kumar N;Kung TA;Patil PG;Chestek CA
• 通讯作者: Chestek CA

A low-power communication scheme for wireless, 1000 channel brain–machine interfaces

• DOI: 10.1088/1741-2552/ac7352
• 发表时间: 2022-03
• 期刊: Journal of Neural Engineering
• 影响因子: 4
• 作者: Joseph T. Costello;Samuel R. Nason-Tomaszewski;Hyochan An;Jungho Lee;Matthew J. Mender;Hisham Temmar;Dylan M. Wallace;Jongyup Lim;Matthew S. Willsey;Parag G. Patil;Taekwang Jang;Jamie D. Phillips;Hun-Seok Kim;D. Blaauw;C. Chestek