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Noninvasive neuroimaging enhances continuous neural tracking for robotic device control.
Science Robotics ( IF 25.0 ) Pub Date : 2019-06-19 , DOI: 10.1126/scirobotics.aaw6844
B J Edelman 1 , J Meng 2 , D Suma 2 , C Zurn 1 , E Nagarajan 3 , B S Baxter 1 , C C Cline 1 , B He 1, 2
Affiliation  

Noninvasive neuroimaging and increased user engagement improve EEG-based neural decoding and facilitate real-time 2D robotic device control. Brain-computer interfaces (BCIs) using signals acquired with intracortical implants have achieved successful high-dimensional robotic device control useful for completing daily tasks. However, the substantial amount of medical and surgical expertise required to correctly implant and operate these systems greatly limits their use beyond a few clinical cases. A noninvasive counterpart requiring less intervention that can provide high-quality control would profoundly improve the integration of BCIs into the clinical and home setting. Here, we present and validate a noninvasive framework using electroencephalography (EEG) to achieve the neural control of a robotic device for continuous random target tracking. This framework addresses and improves upon both the “brain” and “computer” components by increasing, respectively, user engagement through a continuous pursuit task and associated training paradigm and the spatial resolution of noninvasive neural data through EEG source imaging. In all, our unique framework enhanced BCI learning by nearly 60% for traditional center-out tasks and by more than 500% in the more realistic continuous pursuit task. We further demonstrated an additional enhancement in BCI control of almost 10% by using online noninvasive neuroimaging. Last, this framework was deployed in a physical task, demonstrating a near-seamless transition from the control of an unconstrained virtual cursor to the real-time control of a robotic arm. Such combined advances in the quality of neural decoding and the practical utility of noninvasive robotic arm control will have major implications for the eventual development and implementation of neurorobotics by means of noninvasive BCI.

中文翻译:

无创神经成像增强了机器人设备控制的连续神经跟踪。

无创神经成像和增加的用户参与改善了基于脑电图的神经解码并促进实时 2D 机器人设备控制。使用皮质内植入物获取的信号的脑机接口(BCI)已经成功实现了高维机器人设备控制,有助于完成日常任务。然而,正确植入和操作这些系统所需的大量医疗和外科专业知识极大地限制了它们在少数临床病例之外的使用。一种需要较少干预、能够提供高质量控制的非侵入性对应物将极大地改善脑机接口与临床和家庭环境的整合。在这里,我们提出并验证了一种使用脑电图(EEG)的无创框架,以实现机器人设备的神经控制,以进行连续随机目标跟踪。该框架分别通过连续追求任务和相关训练范例增加用户参与度,并通过脑电图源成像提高非侵入性神经数据的空间分辨率,从而解决并改进了“大脑”和“计算机”组件。总而言之,我们独特的框架将传统中心向外任务的 BCI 学习能力提高了近 60%,而在更现实的连续追踪任务中则将 BCI 学习能力提高了 500% 以上。我们进一步证明,通过使用在线无创神经影像,BCI 控制可额外增强近 10%。最后,该框架被部署在物理任务中,展示了从不受约束的虚拟光标的控制到机械臂的实时控制的近乎无缝的过渡。神经解码质量的进步和无创机器人手臂控制的实用性的结合将对通过无创脑机接口的神经机器人技术的最终开发和实施产生重大影响。
更新日期:2019-06-19
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