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

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，使用他们的脑电波进行通信和控制设备的系统。 在美国，有超过200万人患有这种疾病，其中许多人可以从BCI控制的辅助设备中受益匪浅。 在过去的两年中，已经证明，一个非侵入性的，头皮记录的脑电图（EEG）为基础的BCI范例可以使用一个残疾人的个人电脑的长期，可靠的控制。 这种BCI范例允许用户通过对所产生的诱发脑反应进行分类，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确定用于上述演示的相同BCI范例和技术可以直接实现以生成用于根据用户意图在三维中控制机器人操纵器的高级命令，并且这种BCI可以提供优于当前可用的替代BCI技术的上级维度控制，脑皮层电图（ECoG），直接从大脑表面记录的电活动，在最近的初步工作中已经被证明是BCI的另一种潜在可行的控制。 与EEG相比，ECoG已被证明具有上级信噪比以及空间和频谱特性。 但是目前用于操作脑机接口的脑电信号还没有在皮层脑电图中得到表征。 PI认为ECoG信号可用于提高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，使用它们作为控制信号来操作模拟机器人操纵器，并评估两种记录模式之间的控制水平（速度和准确性），并将结果与竞争性BCI技术进行比较。 由于这是与杰克逊维尔的马约诊所的神经内科和神经外科的合作项目，PI团队将有机会接触ECoG网格患者库，并从中招募本研究的受试者。这项研究将在新兴的BCI领域做出许多贡献，因此将为严重残疾者提供新的自主性水平，与他人沟通和执行日常任务，这将最终大大提高他们的生活质量。

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.