HCC: Medium: Collaborative Research: Improved Control and Sensory Feedback for Neuroprosthetics

HCC：中：合作研究：改进神经假体的控制和感觉反馈

• 批准号: 1065027
• 负责人: Richard Gillespie
• 金额: $ 39万
• 依托单位: Regents of the University of Michigan - Ann Arbor
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-06-01 至 2017-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1065027&HistoricalAwards=false
• 关键词: HCC; Medium; Collaborative; Research; Improved

This research involves collaboration among investigators at four institutions. Recent advances in motor behavior have uncovered structure in the supporting neural control architecture, including distinctions between feed-forward and feedback control functions and learning. While the neural code has not yet been cracked, much is now known about how its foundations for sensorimotor control differ from those of even the most modern computer-based algorithms. For example, neural function must accommodate transmission and processing delays, so feedback control is subservient to feed-forward and anticipatory control. The nervous system produces exquisite, constantly and widely available predictions concerning body and environment interactions. These predictive models (also called internal models) are constructed by learning the invariants in the mapping from motor commands to sensory feedback (and inverses thereof). The PIs have developed a unique approach based upon readings from a scalp array of EEG electrodes for the construction of algorithms (decoders) which predict motor behavior (control signals) as a weighted sum of the EEG data from all electrodes at multiple time lags. The team has demonstrated two-axis control over a screen cursor using only 10 minutes of EEG and motion training data, a feat far surpassing any brain-computer interface (BCI) available to date. In the current project, the team will build upon this prior work to design and validate noninvasive neural decoders that generate agile control in upper limb prosthetics. To this end, they will investigate neural correlates of brain adaptation to multiple sources of feedback using EEG and functional near infrared spectroscopy (fNIR). An important challenge will be to provide sensory feedback appropriate to contact tasks performed with a prosthesis. Existing BCIs and neuro-prosthetic devices rely at best on vibrotactile feedback and often only on visual feedback. The PIs will add haptic and proprioceptive feedback in concert with a novel adaptation of vibrotactile, skin stretch, and arm squeeze technologies in the prosthesis interface, to provide intuitive control over contact tasks and to strengthen the motor imagery whose neural correlates are processed by the EEG decoder. To establish baseline measures, the team will compare prosthetic performance under direct brain control to myoelectric prosthetic control and direct manual control. Experiments will be performed involving both able-bodied individuals and amputees, in which real-time decoding (EEG) and analysis (EEG/fNIR) of sensorimotor control and cognitive load will be combined.Broader Impacts: This research will revolutionize the control and interface of upper limb prosthetics. The work will lead to a better understanding of the role of sensory feedback in brain-computer interfaces and will lay the foundation for restoration of motor and sensory function for amputees and individuals with neurological disease. The project will create a unique interdisciplinary environment enabling education, training, co-advising and exchange of graduate students, course development, and involvement of undergraduates in research. The PIs will also participate in outreach activities on their various campuses, targeting underrepresented groups in science and engineering.

这项研究涉及四个机构的研究人员之间的合作。运动行为的最新进展揭示了支持神经控制结构的结构，包括前馈和反馈控制功能和学习之间的区别。虽然神经密码还没有被破解，但人们现在已经知道，它的感觉运动控制基础与甚至是最现代的基于计算机的算法有何不同。例如，神经功能必须适应传输和处理延迟，因此反馈控制从属于前馈控制和预期控制。神经系统对身体和环境的相互作用做出精准、持续和广泛可用的预测。这些预测模型(也称为内部模型)是通过学习从运动指令到感觉反馈(及其反转)映射中的不变量来构建的。PI已经开发了一种基于来自EEG电极的头皮阵列的读数的独特方法，用于构建算法(解码器)，该算法将运动行为(控制信号)预测为来自所有电极的在多个时间滞后的EEG数据的加权和。该团队仅使用10分钟的脑电和运动训练数据就展示了对屏幕光标的两轴控制，这一壮举远远超过了迄今可用的任何脑机接口(BCI)。在目前的项目中，该团队将在先前工作的基础上，设计和验证在上肢假肢中产生灵活控制的非侵入性神经解码器。为此，他们将使用脑电和功能性近红外光谱(FNIR)来研究大脑对多种反馈来源的适应的神经相关性。一个重要的挑战将是提供适当的感觉反馈，以执行与假体执行的接触任务。现有的脑机接口和神经假体设备充其量依赖于振动触觉反馈，而且往往只依赖视觉反馈。PI将添加触觉和本体感觉反馈，与假肢界面中的振动触觉、皮肤拉伸和手臂挤压技术相结合，以提供对接触任务的直观控制，并加强运动图像，其神经关联由EEG解码器处理。为了建立基线测量，研究小组将比较直接大脑控制下的假肢性能与肌电假体控制和直接手动控制下的假肢性能。实验将包括健全的个体和截肢者，其中将结合感觉运动控制和认知负荷的实时解码(EEG)和分析(EEG/FNIR)。广泛的影响：这项研究将彻底改变上肢假肢的控制和接口。这项工作将使人们更好地理解感觉反馈在脑机接口中的作用，并将为截肢者和神经疾病患者恢复运动和感觉功能奠定基础。该项目将创造一个独特的跨学科环境，使研究生的教育、培训、共同建议和交流、课程开发和本科生参与研究成为可能。PIS还将在其各个校园参加外展活动，目标是科学和工程领域代表性不足的群体。