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

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以及学习之间的区别。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，因此反馈控制服从于前馈和预期控制。神经系统对身体和环境的相互作用产生精细的、持续的、广泛可用的预测。这些预测模型（也称为内部模型）是通过学习从运动命令到感觉反馈（及其逆）映射中的不变量来构建的。pi开发了一种独特的方法，该方法基于头皮脑电图电极阵列的读数，用于构建算法（解码器），该算法将多个时间滞后的所有电极的脑电图数据加权求和来预测运动行为（控制信号）。该团队仅使用10分钟的脑电图和运动训练数据就演示了对屏幕光标的两轴控制，这一壮举远远超过了迄今为止可用的任何脑机接口（BCI）。在当前的项目中，该团队将以先前的工作为基础，设计和验证非侵入性神经解码器，以实现上肢假肢的敏捷控制。为此，他们将利用脑电图和功能性近红外光谱（fNIR）研究大脑适应多种反馈源的神经相关性。一个重要的挑战将是提供适当的感官反馈，以接触与假体执行的任务。现有的脑机接口和神经假肢设备最多依赖于振动触觉反馈，通常只依赖于视觉反馈。pi将在假体界面中加入触觉和本体感觉反馈，并结合振动触觉、皮肤拉伸和手臂挤压技术的新适应，提供对接触任务的直观控制，并加强由脑电图解码器处理的神经相关的运动图像。为了建立基线测量，研究小组将比较直接大脑控制、肌电控制和直接手动控制下假肢的性能。实验将在健全人和截肢者中进行，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）。更广泛的影响：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，并为截肢者和神经系统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，促进研究生的教育、培训、共同指导和交流、课程开发以及本科生参与研究。这些学院还将在各自的校园里参与外联活动，针对科学和工程领域代表性不足的群体。

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.