CRCNS: Hybrid non-invasive brain-machine interfaces for 3D object manipulation

CRCNS：用于 3D 对象操作的混合非侵入性脑机接口

基本信息

批准号：
8507287
负责人：
Emanuel Todorov
金额：
$ 24.04万
依托单位：
UNIVERSITY OF WASHINGTON
依托单位国家：
美国
项目类别：
财政年份：
2010
资助国家：
美国
起止时间：
2010-07-01 至 2014-06-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/8507287
关键词：
Address Algorithms Amputees Behavior Brain Communication Complex Data Data Analyses Data Collection Data Set Devices Dose Electrodes Electroencephalography Epilepsy Eye Movements Feedback Goals Hand Head Movements Home environment Hybrids Instruction Knowledge Learning Left Life Lifting Machine Learning Magnetic Resonance Imaging Manuals Measurement Methodology Methods Modeling Motor Motor Activity Movement Movement Disorders Neck Patients Physical environment Positioning Attribute Prosthesis Robot Robotics Scheme Science Shapes Signal Transduction Simulate Source Specific qualifier value Spinal cord injury Stroke Testing Text Time Upper Extremity Work arm brain machine interface design grasp instrument interest kinematics visual feedback

Address Algorithms Amputees Behavior Brain Communication Complex Data Data Analyses Data Collection Data Set Devices Dose Electrodes Electroencephalography Epilepsy Eye Movements Feedback Goals Hand Head Movements Home environment Hybrids Instruction Knowledge Learning Left Life Lifting Machine Learning Magnetic Resonance Imaging Manuals Measurement Methodology Methods Modeling Motor Motor Activity Movement Movement Disorders Neck Patients Physical environment Positioning Attribute Prosthesis Robot Robotics Scheme Science Shapes Signal Transduction Simulate Source Specific qualifier value Spinal cord injury Stroke Testing Text Time Upper Extremity Work arm brain machine interface design grasp instrument interest kinematics visual feedback

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项目摘要

Interacting with the physical environment and manipulating objects is an essential part of daily life. This ability is lost in upper-limb amputees as well as patients with spinal cord injury, stroke, ALS and other movement disorders. These people know what they want to do as well as how they would do it if their arms were functional. If such knowledge is decoded and sent to a prosthetic arm (or to the patient's own arm fitted with functional electric stimulators) the lost motor function could be restored. The decoding is unlikely to be perfect however the brain can adapt to an imperfect decoder using real-time feedback. Several groups including ours have recently demonstrated that at least in principle this can be achieved. However, as is often the case in science, the initial work has been done in idealized conditions and its applicability to real-world usage scenarios remains an open question. The goal of this project is to bring movement control brain-machine interfaces (BMIs) closer to helping the people who need them, and at the same time exploit the rich datasets we collect in order to advance our understanding of sensorimotor control and learning. This will be accomplished by creating hybrid BMIs which exploit information from multiple sources, combined with modern algorithms from machine learning and automatic control. RELEVANCE (See instructions): Being able to interact with the physical environment and manipulate objects is an essential part of daily life. Brain-machine interfaces are one way to restore this ability to patients who have lost it. The proposed project will bring brain-machine interfaces closer to helping patients in real-worid object manipulation tasks.

与物理环境互动和操纵物体是日常生活的重要组成部分。这能力在上limb amputees以及脊髓损伤，中风，ALS和其他患者中丧失运动障碍。这些人知道他们想做什么以及如果他们的手臂会如何做功能性。如果将这些知识解码并发送到假肢（或安装患者的手臂使用功能性刺激器）可以恢复运动功能。解码不太可能是完美的大脑可以使用实时反馈适应不完善的解码器。几个小组包括我们的包括我们最近表明，至少在原则上可以实现这一目标。但是，按原样通常在科学的情况下，最初的工作是在理想化的条件下进行的现实世界的用法方案仍然是一个悬而未决的问题。该项目的目的是带来运动控制脑机界面（BMI）更接近帮助需要它们的人，同时利用我们收集的丰富数据集是为了促进我们对感觉运动控制和学习的理解。这将通过创建混合BMI来实现，从而利用多个来源的信息，并结合使用机器学习和自动控制的现代算法。相关性（请参阅说明）：能够与物理环境和操纵物体互动是日常生活的重要组成部分。脑机界面是恢复失去患者能力的一种方法。提议项目将使脑机界面更接近帮助患者进行现实的对象操纵任务。