HCC: Medium: RUI: Control of a Robotic Manipulator via a Brain-Computer Interface

HCC：中：RUI：通过脑机接口控制机器人操纵器

• 批准号: 0905468
• 负责人: Dean Krusienski
• 金额: $ 74.27万
• 依托单位: University of North Florida
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-07-15 至 2010-11-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0905468&HistoricalAwards=false
• 关键词: HCC; Medium; RUI; Control; Robotic

A brain-computer interface (BCI) is a system that allows users, especially individuals with severe neuromuscular disorders, to communicate and control devices using their brain waves. There are over two million people in the United States afflicted by such disorders, many of whom could greatly benefit from assistive devices controlled by a BCI. Over the past two years, it has been demonstrated that a non-invasive, scalp-recorded electroencephalography (EEG) based BCI paradigm can be used by a disabled individual for long-term, reliable control of a personal computer. This BCI paradigm allows users to select from a set of symbols presented in a flashing visual matrix by classifying the resulting evoked brain responses. One of the goals of this project is to establish that the same BCI paradigm and techniques used for the aforementioned demonstration can be straightforwardly implemented to generate high-level commands for controlling a robotic manipulator in three dimensions according to user intent, and that such a BCI can provide superior dimensional control over alternative BCI techniques currently available, as well as a wider variety of practical functions for performing everyday tasks.Electrocorticography (ECoG), electrical activity recorded directly from the surface of the brain, has been demonstrated in recent preliminary work to be another potentially viable control for a BCI. ECoG has been shown to have superior signal-to-noise ratio, and spatial and spectral characteristics, compared to EEG. But the EEG signals used at present to operate BCIs have not been characterized in ECoG. The PI believes ECoG signals can be used to improve the speed and accuracy of BCI applications, including for example control of a robotic manipulator. Thus, additional goals of this project are to characterize evoked responses obtained from ECoG, to use them as control signals to operate a simulated robotic manipulator, and to assess the level of control (speed and accuracy) between the two recording modalities and compare the results to competitive BCI techniques. Because this is a collaborative effort with the Departments of Neurology and Neurosurgery at the Mayo Clinic in Jacksonville, the PI team will have access to a pool of ECoG grid patients from which to recruit participants for this study.Broader Impacts: This research will make a number of contributions in the emerging field of BCI and thus will serve as a step toward providing severely disabled individuals with a new level of autonomy for communicating with others and for performing everyday tasks, which will ultimately dramatically improve their quality of life.

脑机接口（BCI）是一种允许用户，特别是患有严重神经肌肉疾病的人，使用脑电波进行交流和控制设备的系统。美国有200多万人患有这类疾病，其中许多人可以从脑机接口控制的辅助装置中受益匪浅。在过去的两年中，已经证明了一种非侵入性的、基于头皮记录脑电图（EEG）的脑机接口模式可以被残疾人用于长期、可靠地控制个人计算机。这种脑机接口模式允许用户通过对引起的大脑反应进行分类，从闪烁的视觉矩阵中呈现的一组符号中进行选择。该项目的目标之一是建立上述演示中使用的相同的BCI范式和技术，可以直接实现，以根据用户意图生成三维控制机器人机械手的高级命令，并且这样的BCI可以提供优于当前可用的替代BCI技术的维度控制，以及执行日常任务的更广泛的实用功能。皮质电图（ECoG）是直接从大脑表面记录的电活动，在最近的初步工作中已被证明是脑机接口的另一种潜在可行的控制方法。与脑电图相比，ECoG具有优越的信噪比、空间和频谱特征。但目前用于操作脑机接口的脑电图信号尚未在脑电图中表征。PI认为ECoG信号可以用于提高BCI应用的速度和准确性，包括例如机器人操纵器的控制。因此，该项目的其他目标是表征从ECoG获得的诱发反应，将其作为控制信号来操作模拟机器人机械手，并评估两种记录方式之间的控制水平（速度和准确性），并将结果与竞争性脑机接口技术进行比较。由于这是与杰克逊维尔梅奥诊所神经内科和神经外科的合作，PI团队将可以访问ECoG网格患者池，从中招募参与者进行这项研究。更广泛的影响：这项研究将在新兴的脑机接口领域做出许多贡献，因此将为严重残疾人提供一个新的自主水平，用于与他人交流和执行日常任务，最终将极大地提高他们的生活质量。