Brain-Robot Interface: A Robust, High Performance Predictive Control Algorithm

描述（由申请人提供）： 项目摘要：拟议研究的目的是开发一种改进的“思想引导”机器人控制算法，使用人脑的脑电图（EEG）来执行机器人设备的某些动作。我认为，现有脑机接口（BMI）用于帮助患有神经系统疾病的人进行运动活动而开发的性能有限的原因之一是由于大脑信号的动力学过于复杂，现有的控制算法无法有效发挥作用。为了克服这个问题，我设想神经元动态模型识别和控制算法开发的范式转变，这将有助于更好地理解复杂的神经元系统并为控制系统提供改进的性能。在拟议的项目中，我计划研究以下假设：1）基于模型的预测方法和最优控制策略将比现有的非基于模型和非最优方法显着改善 BMI 的信号提取和控制性能。我在这里提出了一种两部分方法：1）响应音频/视觉提示获取非侵入性人类头皮脑电图，并使用现有和提出的基于模型的技术比较和对比脑电图特征，以便开发数据驱动的状态空间格式的经验模型，2）设计一种MFC算法，与常用的滤波和/或比例反馈控制算法不同，它将预测机器人设备在未来一段时间内（称为预测范围）的所需运动，将使用优化算法来计算当前的控制移动采样时间，并且能够使用许多控制器参数在线调整，以便有效地控制机器人设备。该 K25 指导职业奖的更广泛目标是将候选人的控制工程背景应用于神经学应用，并开发这两个研究领域之间的接口。除了在宾夕法尼亚州立大学工程科学与力学系进行拟议的研究外，拟议的职业发展计划还包括通过课程、研讨会和其他教学手段进行广泛的培训，这将使候选人为神经学问题解决方案的独立学术职业奠定坚实的基础。 与公共卫生的相关性：拟议研究中开发的技术将通过将现代控制工程与神经生物学相结合，促进对复杂神经生物学行为的理解。这意味着患有脑部或脊髓损伤等神经系统疾病的人将通过开发更智能、更有效的神经假体得到更好的帮助。

DESCRIPTION (provided by applicant): Project Summary: The objective of the proposed research is to develop an improved "thought-guided" robotic control algorithm using electroencephalography (EEG) from the human brain for certain actions to be performed by a robotic device. One of the reasons of limited performance of existing brain machine interfaces (BMI) being developed to assist people with neurological disorders to conduct motor activities, I believe, is due to the fact that the dynamics of brain signals are too complex for the existing control algorithms to be efficient. To overcome this, I envision a paradigm shift in the neuronal dynamic model identification and control algorithm development that will help better understand the complex neuronal systems and provide improved performance to the control system. In the proposed project, I plan to investigate the hypothesis: 1) Model-based predictive approach and optimal control strategies will improve signal extraction and control performance in BMI substantially over existing non-model based and non-optimal approaches. I here propose a two part approach: 1) acquire non-invasive human scalp EEG in response to audio/visual cues, and compare and contrast EEG features using existing and proposed model-based techniques in order to develop a data driven, empirical model in state space format, and 2) design an MFC algorithm which, unlike the commonly used filtering and/or proportional feedback control algorithms, will predict the desired movement of the robotic device over a period of time in the future called the prediction horizon, will use an optimization algorithm to calculate the control move at the current sampling time, and will have the ability to be tuned online using a number of controller parameters in order to efficiently control the robotic device. The broader aim of this K25 mentored career award is to apply the candidate's control engineering background to neurological applications and develop an interface between these two research areas. The proposed career development plan includes, in addition to carrying out the proposed research in the Engineering Science and Mechanics Department at Penn State University, extensive training through courses, workshops, and other didactic means which will allow the candidate to build a strong foundation for an independent academic career in neurological problem solutions. Relevance to Public Health: Technology developed in the proposed research will advance the understanding of the complex neurobiological behavior by merging modern control engineering with neurobiology. It has implications that people with neurological disorders such as brain or spinal cord injuries will be better assisted through the development of smarter, more effective neural prosthetics.