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

PI: Nenadic, Zoran and Do, An h .提案号：1134575问题陈述。脑机接口（BCI）是一种使大脑直接控制外部设备，而不产生任何运动输出的技术。脑机接口技术最大的潜力在于神经康复领域，其最终目标是将脑机接口与肢体假体或肌肉功能性电刺激（FES）相结合，使重度瘫痪患者恢复直观、自然的运动。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一直受到植入物生物不相容性的限制。因此，硬脑膜下皮质电图（ECoG）电极成为一种很有前途的脑机接口信号采集平台。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，同时提供相当数量的有用的运动相关信息。然而，这些信息是否足以用于BCI控制上肢假体，以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，尚未确定。研究计划。本初步研究的主要目的是评估基于脑电图的脑机接口在一小部分癫痫患者中控制假肢的可行性，这些癫痫患者正在接受脑电图电极植入以进行癫痫手术评估。他们将进行一系列已完成的和想象的基本上肢运动，同时记录他们的脑电图、肢体轨迹和肌电图（EMG）数据。然后对这些数据进行分析，并推导出一个预测模型，用于从ECoG信号中实时解码上肢轨迹。然后，该模型将被纳入BCI系统，其性能将通过实时在线控制拟人化机械臂（上肢假体的替代品）进行测试。基于科目？实现BCI控制机器人执行初级和目标导向运动任务的能力，将评估基于ecog的BCI控制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过，因此其可行性尚未经过测试。2)本研究将引入新的实验范式，记录上肢运动的执行性和动觉性图像的脑电图。3)与以往的研究不同，我们的方法将记录运动肌肉的肌电图。从ECoG中预测肌电参数的能力将在未来的研究中有用，这些研究希望将脑机接口与上肢FES设备相结合。4)本研究将有助于理解与上肢假肢实时在线BCI控制相关的不完全理解的脑可塑性和人机协同适应过程。5)该项目将导致一种新型算法的发展，用于统计分析和实时解码ECoG信号。知识价值。该研究将开发新的、最先进的自适应算法，用于分析和实时解码ECoG信号。这些方法将避免在该领域中常用的不必要的假设和临时策略，因此将能够系统地分析高维，统计稀疏，非平稳的ECoG信号。它们也可能适用于广泛类别的时空生物医学信号，也许还有其他类型的统计数据。除了脑机接口的应用外，本研究收集的ECoG、肢体轨迹和肌电图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。本研究的活动将促进本科、研究生和医学生的教育、科学素养和终身学习。具体地说，这项研究的内容将被纳入教学和指导课程。本科生和研究生将参与拟议的研究和教育计划，他们的研究成果将被广泛传播。这将促进他们的领导能力、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。调查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，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.