The Feasibility of Electrocorticogram Brain-Computer Interface for Control of Arm Prostheses

PI：Nenadic，Zoran和Do，一个H.命题编号：1134575Problem陈述。脑机接口(BCI)是一种使大脑能够直接控制外部设备的技术，而不会产生任何运动输出。BCI技术最大的潜力在于神经康复领域，最终目标是将BCI与肢体假肢或肌肉功能电刺激(FES)相结合，以恢复严重瘫痪患者的直观和自然运动。目前最先进的脑机接口使用植入大脑皮层的微电极阵列来获取神经元信号，作为脑机接口控制的来源。然而，由于植入物的生物不相容，该技术在人类神经康复中的应用一直受到限制。因此，硬膜下皮层脑电(ECoG)电极作为一种很有前途的脑机接口信号采集平台应运而生。初步研究表明，ECoG电极产生的信号的长期稳定性优于微电极阵列，同时提供了相当数量的有用电机相关信息。然而，这些信息是否足以用于上肢假体的脑机接口控制，以便执行对人类日常活动有用的目标导向任务，并证明电极植入手术的风险是合理的，还没有确定。研究计划。这项先导性研究的主要目标是评估基于皮层脑电的脑机接口在少数正在接受皮层脑电电极植入的癫痫患者中控制手臂假体的可行性，以进行癫痫手术评估。他们将执行一系列执行和想象的基本上肢动作，同时记录他们的ECoG、肢体轨迹和肌电(EMG)数据。然后将分析该数据，并将导出用于执行从ECoG信号实时解码上肢轨迹的预测模型。然后，这个模型将被整合到脑机接口系统中，其性能将使用拟人机械臂(上肢假肢的替代品)的实时在线控制进行测试。根据受试者？为了实现机器人的脑机接口控制以执行基本的和面向目标的运动任务，将评估基于ECoG的脑机接口用于手臂假肢控制的可行性。新颖性：1)拟人机械臂与基于ECoG的脑机接口的集成从未实现，因此其可行性仍未得到测试。2)本研究将引入新的实验范式，即ECoG将被记录在对上肢运动的执行和运动表象的反应中。3)与以往的研究不同，我们的方法将记录参与运动的肌肉的肌电。根据ECoG预测肌电参数的能力将在未来渴望将BCI与上肢FES设备相结合的研究中发挥作用。4)这项研究将有助于理解与实时在线BCI控制上肢假肢相关的未完全了解的大脑可塑性和人机共同适应过程。5)该项目将导致开发一类用于ECoG信号的统计分析和实时解码的新算法。这项拟议的研究将开发新的、最先进的自适应算法，用于ECoG信号的分析和实时解码。这些方法将避免该领域中常用的不必要的假设和特别策略，并因此将使得能够以系统的方式分析高维、统计稀疏、非平稳的ECoG信号。它们也可以适用于一大类时空生物医学信号，也许还适用于其他类型的统计数据。除了脑机接口的应用，在拟议的研究中收集的皮层脑电、肢体轨迹和肌电数据将对人类运动控制理论的发展做出重大贡献。这项研究活动将促进本科生、研究生和医学生的教育、科学素养和终身学习。具体而言，这项研究的内容将纳入教学和辅导课程。本科生和研究生将参与拟议的研究和教育计划，他们的研究结果将广泛传播。这将促进他们的领导力、跨学科和研究技能的发展。拟议的活动还将扩大在工程和科学领域任职人数不足的群体的参与。调查人员将通过开展演讲、演示和展览等教育活动，促进K-12少数族裔学生的大学教育和对工程/科学职业的追求。此外，调查人员将积极参与高需求学区K-12数学和科学教师的专业发展，目标是提高他们的保留率和领导技能。

PI: Nenadic, Zoran and Do, An H.Proposal Number: 1134575Problem Statement. Brain-computer interface (BCI) is a technology that enables direct brain control of external devices, without generating any motor outputs. BCI technology's greatest potential lies in the field of neuro-rehabilitation, with the ultimate goal of integrating BCI with limb prostheses or functional electrical stimulation (FES) of muscles to restore intuitive and natural movements to individuals with severe paralysis. Current state-of-the-art BCIs use micro-electrode arrays, implanted in the cortex, to acquire neuronal signals as the source of BCI control. However, application of this technology to human neuro-rehabilitation has been limited by the bio-incompatibility of the implant. Consequently, subdural electrocorticogram (ECoG) electrodes emerged as a promising BCI signal acquisition platform. Preliminary studies suggest that ECoG electrodes yield signals whose long-term stability properties are superior to those of microelectrode arrays, while providing a comparable amount of useful motor-related information. However, whether this information is sufficient for BCI control of an upper extremity prosthesis so as to perform goal-oriented tasks useful for human daily activities and justify the risks of electrode implantation surgery, has not been established. Research Plan. The primary goal of this pilot study is to assess the feasibility of ECoG-based BCI for control of arm prostheses in a small population of epilepsy patients who are undergoing ECoG electrodes implantation for epilepsy surgery evaluation. They will perform a series of executed and imagined elementary upper extremity movements while their ECoG, limb trajectories, and electromyogram (EMG) data will be recorded. This data will then be analyzed, and a predictive model to perform real-time decoding of upper extremity trajectories from ECoG signals will be derived. This model will then be incorporated into a BCI system, whose performance will be tested using real-time online control of an anthropomorphic robotic arm (a stand-in for an upper extremity prosthesis). Based on subjects? ability to achieve BCI control of the robot to perform elementary and goal-oriented motor tasks, the feasibility of ECoG-based BCIs for control of arm prostheses control will be assessed. Novelty: 1) The integration of an anthropomorphic robotic arm with an ECoG-based BCI has never been realized, and so its feasibility remains untested. 2) The study will introduce novel experimental paradigms in that ECoG will be recorded in response to both executed and kinesthetic imagery of upper extremity movements. 3) Unlike prior studies, our approach will record EMG of muscles involved in movements. The ability to predict EMG parameters from ECoG will be useful in future studies that aspire to integrate BCIs with upper extremity FES devices. 4) The study will contribute to understanding of the incompletely understood brain plasticity and human-computer co-adaptation processes associated with real-time online BCI control of upper extremity prostheses. 5) The project will lead to the development of a novel class of algorithms for statistical analysis and real-time decoding of ECoG signals.Intellectual Merit. The proposed study will develop novel, state-of-the-art, adaptive algorithms for analysis and real-time decoding of ECoG signals. These methods will avoid unnecessary assumptions and ad hoc strategies, commonly used in this field, and will therefore enable a systematic way of analyzing highdimensional, statistically sparse, nonstationary ECoG signals. They may also be applicable to a wide class spatio-temporal biomedical signals, and perhaps other types of statistical data. Aside from BCI applications, the ECoG, limb trajectory, and EMG data collected in the proposed study will contribute significantly to development of human motor control theory.Broader Impacts. The activities of this study will promote the education, scientific literacy and lifelong learning in undergraduate, graduate, and medical students. Specifically, elements of the study will be integrated into the teaching and mentoring curricula. Undergraduate and graduate students will participate in the proposed research and educational plans and their findings will be broadly disseminated. This will foster the development of their leadership, interdisciplinary, and research skills. The proposed activities will also broaden the participation of underrepresented groups in engineering and science. The investigators will promote college education and the pursuit of engineering/science careers in minority K-12 students by developing educational activities such as presentations, demonstrations, and exhibits. Additionally, the investigators will actively participate in the professional development of K-12 math and science teachers in high-need school districts, with the goal of improving their retention rates and leadership skills.