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

• 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
• 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

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.

与物理环境交互和操纵物体是日常生活的重要组成部分。这 上肢截肢者以及脊髓损伤、中风、ALS和其他疾病患者的能力丧失。 运动障碍。这些人知道他们想做什么，也知道如果他们的手臂 是可行的。如果这种知识被解码并发送到假肢（或患者自己的手臂安装 使用功能性电刺激器）可以恢复失去的运动功能。解码不太可能是 然而，大脑可以使用实时反馈来适应不完美的解码器。几组 包括我们的国家最近都表明，至少在原则上这是可以实现的。然而， 通常情况下，在科学，最初的工作已经完成了理想化的条件和适用性， 现实世界的使用场景仍然是一个悬而未决的问题。这个项目的目标是把运动控制 脑机接口（BMI）更接近于帮助需要它们的人，同时利用 我们收集了丰富的数据集，以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，该混合BMI利用来自多个来源的信息， 现代算法从机器学习和自动控制。 相关性（参见说明）： 能够与物理环境交互并操纵物体是日常生活的重要组成部分。 脑机接口是恢复失去这种能力的患者的一种方法。 该项目将使脑机接口更接近于帮助患者完成现实世界的物体操作任务。