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

皮质电图脑机接口控制手臂假肢的可行性

• 批准号: 1134575
• 负责人: Zoran Nenadic
• 金额: $ 24.63万
• 依托单位: University of California-Irvine
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-10-01 至 2015-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1134575&HistoricalAwards=false
• 关键词: Feasibility; Electrocorticogram; Brain; Computer; Interface

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.

PI：Nenadic、Zoran 和 Do，H.提案编号：1134575 问题陈述。脑机接口（BCI）是一种能够让大脑直接控制外部设备而不产生任何电机输出的技术。 BCI技术的最大潜力在于神经康复领域，其最终目标是将BCI与肢体假肢或肌肉功能性电刺激（FES）相结合，以恢复严重瘫痪患者的直觉和自然运动。目前最先进的脑机接口使用植入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，该技术在人类神经康复中的应用受到植入物的生物不相容性的限制。因此，硬膜下皮质电图 (ECoG) 电极成为一种有前途的 BCI 信号采集平台。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的运动相关信息。 然而，这些信息是否足以通过脑机接口控制上肢假肢，从而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，目前尚未确定。研究计划。这项试点研究的主要目标是评估基于 ECoG 的 BCI 用于控制一小部分癫痫患者的手臂假肢的可行性，这些患者正在接受 ECoG 电极植入以进行癫痫手术评估。他们将执行一系列实际执行和想象的基本上肢运动，同时记录他们的 ECoG、肢体轨迹和肌电图 (EMG) 数据。然后对这些数据进行分析，并导出一个预测模型，用于根据 ECoG 信号对上肢轨迹进行实时解码。然后，该模型将被整合到脑机接口系统中，该系统的性能将通过拟人机械臂（上肢假肢的替代品）的实时在线控制进行测试。根据科目？为了实现机器人 BCI 控制以执行基本和目标导向的运动任务的能力，将评估基于 ECoG 的 BCI 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BCI的集成从未实现过，因此其可行性尚未经过测试。 2）该研究将引入新的实验范式，其中 ECoG 将根据上肢运动的执行图像和动觉图像进行记录。 3）与之前的研究不同，我们的方法将记录参与运动的肌肉的肌电图。从 ECoG 预测 EMG 参数的能力对于未来希望将 BCI 与上肢 FES 设备集成的研究非常有用。 4) 该研究将有助于理解与上肢假肢实时在线 BCI 控制相关的尚未完全了解的大脑可塑性和人机协同适应过程。 5) 该项目将导致开发一类新型算法，用于 ECoG 信号的统计分析和实时解码。智力优点。拟议的研究将开发新颖、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常用的不必要的假设和临时策略，因此将能够以系统的方式分析高维、统计稀疏、非平稳 ECoG 信号。它们还可能适用于广泛的时空生物医学信号，或许还适用于其他类型的统计数据。除了BCI应用之外，拟议研究中收集的ECoG、肢体轨迹和EMG数据将对人类运动控制理论的发展做出重大贡献。更广泛的影响。这项研究的活动将促进本科生、研究生和医学生的教育、科学素养和终身学习。具体来说，研究的要素将被纳入教学和指导课程中。本科生和研究生将参与拟议的研究和教育计划，他们的研究结果将得到广泛传播。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代表性不足的群体在工程和科学领域的参与。研究人员将通过开展演讲、演示和展览等教育活动，促进少数族裔 K-12 学生的大学教育和对工程/科学职业的追求。此外，研究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。