Dept. of Biomedical Engineering, Johns Hopkins Univ, Baltimore, MD, USA.
J Neurophysiol. 2013 Jun;109(12):3067-81. doi: 10.1152/jn.01038.2011. Epub 2013 Mar 27.
The performance of brain-machine interfaces (BMIs) that continuously control upper limb neuroprostheses may benefit from distinguishing periods of posture and movement so as to prevent inappropriate movement of the prosthesis. Few studies, however, have investigated how decoding behavioral states and detecting the transitions between posture and movement could be used autonomously to trigger a kinematic decoder. We recorded simultaneous neuronal ensemble and local field potential (LFP) activity from microelectrode arrays in primary motor cortex (M1) and dorsal (PMd) and ventral (PMv) premotor areas of two male rhesus monkeys performing a center-out reach-and-grasp task, while upper limb kinematics were tracked with a motion capture system with markers on the dorsal aspect of the forearm, hand, and fingers. A state decoder was trained to distinguish four behavioral states (baseline, reaction, movement, hold), while a kinematic decoder was trained to continuously decode hand end point position and 18 joint angles of the wrist and fingers. LFP amplitude most accurately predicted transition into the reaction (62%) and movement (73%) states, while spikes most accurately decoded arm, hand, and finger kinematics during movement. Using an LFP-based state decoder to trigger a spike-based kinematic decoder [r = 0.72, root mean squared error (RMSE) = 0.15] significantly improved decoding of reach-to-grasp movements from baseline to final hold, compared with either a spike-based state decoder combined with a spike-based kinematic decoder (r = 0.70, RMSE = 0.17) or a spike-based kinematic decoder alone (r = 0.67, RMSE = 0.17). Combining LFP-based state decoding with spike-based kinematic decoding may be a valuable step toward the realization of BMI control of a multifingered neuroprosthesis performing dexterous manipulation.
脑机接口 (BMI) 持续控制上肢神经假体的性能可能受益于区分姿势和运动期，以防止假体的不当运动。然而，很少有研究调查如何自主解码行为状态和检测姿势与运动之间的转换，以触发运动学解码器。我们记录了两只雄性恒河猴在执行中心外伸手抓握任务时，来自初级运动皮层 (M1) 和背侧 (PMd) 和腹侧 (PMv) 前运动区的微电极阵列的同时神经元集合和局部场电位 (LFP) 活动，而上肢运动学则通过带有标记的运动捕捉系统进行跟踪前臂、手和手指的背侧。训练状态解码器以区分四种行为状态（基线、反应、运动、握持），同时训练运动解码器以连续解码手端点位置和手腕和手指的 18 个关节角度。LFP 幅度最准确地预测了进入反应（62%）和运动（73%）状态的转变，而尖峰最准确地解码了运动过程中的手臂、手和手指运动学。使用基于 LFP 的状态解码器来触发基于尖峰的运动学解码器 [r = 0.72，均方根误差 (RMSE) = 0.15] 与基于尖峰的状态解码器与基于尖峰的运动学解码器的组合 (r = 0.70，RMSE = 0.17) 或单独的基于尖峰的运动学解码器 (r = 0.67，RMSE = 0.17) 相比，显著提高了从基线到最终握持的伸手抓握运动的解码。结合基于 LFP 的状态解码和基于尖峰的运动学解码可能是实现多手指神经假体进行灵巧操作的 BMI 控制的一个有价值的步骤。
