Libey Tyler, Fetz Eberhard E
Department of Bioengineering, University of WashingtonSeattle, WA, United States.
Department of Physiology and Biophysics, University of WashingtonSeattle, WA, United States.
Front Neurosci. 2017 May 16;11:265. doi: 10.3389/fnins.2017.00265. eCollection 2017.
We describe a low-cost system designed to document bodily movement and neural activity and deliver rewards to monkeys behaving freely in their home cage. An important application is to studying brain-machine interface (BMI) systems during free behavior, since brain signals associated with natural movement can differ significantly from those associated with more commonly used constrained conditions. Our approach allows for short-latency (<500 ms) reward delivery and behavior monitoring using low-cost off-the-shelf components. This system interfaces existing untethered recording equipment with a custom hub that controls a cage-mounted feeder. The behavior monitoring system uses a depth camera to provide real-time, easy-to-analyze, gross movement data streams. In a proof-of-concept experiment we demonstrate robust learning of neural activity using the system over 14 behavioral sessions.
我们描述了一种低成本系统,该系统旨在记录身体运动和神经活动,并向在其家笼中自由行为的猴子提供奖励。一个重要的应用是在自由行为期间研究脑机接口(BMI)系统,因为与自然运动相关的脑信号可能与那些与更常用的受限条件相关的脑信号有显著差异。我们的方法允许使用低成本的现成组件进行短潜伏期(<500毫秒)的奖励发放和行为监测。该系统将现有的无系绳记录设备与一个定制集线器相连接,该集线器控制安装在笼子上的喂食器。行为监测系统使用深度相机来提供实时、易于分析的总体运动数据流。在一个概念验证实验中,我们展示了在14个行为阶段使用该系统对神经活动进行的稳健学习。