State-based decoding of hand and finger kinematics using neuronal ensemble and LFP activity during dexterous reach-to-grasp movements
State-based decoding of hand and finger kinematics using neuronal ensemble and LFP activity during dexterous reach-to-grasp movements
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DOI：
10.1152/jn.01038.2011
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发表时间：
2013-06-01
影响因子：
2.5
通讯作者：
Thakor, Nitish V.
中科院分区：
文献类型：
--
作者：
Aggarwal, Vikram;Mollazadeh, Mohsen;Thakor, Nitish V.
连续控制上肢神经假体的脑机接口（BMI）的性能可能受益于区分姿势和运动的周期，以防止假体的不适当运动。然而，很少有研究调查了如何解码行为状态和检测姿势和运动之间的转换可以自主触发运动解码器。我们记录了同时神经元合奏和局部场电位（LFP）的活动，从微电极阵列在初级运动皮层（M1）和背侧（PMd）和腹侧（PMv）运动前区的两只雄性恒河猴执行中心外的达到和把握任务，而上肢运动学跟踪与运动捕捉系统的前臂，手和手指的背侧方面的标记。训练状态解码器以区分四种行为状态（基线、反应、运动、保持），而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振幅最准确地预测了反应（62%）和运动（73%）状态的过渡，而尖峰最准确地解码手臂，手和手指运动在运动过程中。使用基于LFP的状态解码器来触发基于尖峰的运动解码器[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解码器组合相比，显著改善了从基线到最终保持的伸手抓握运动的解码（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）。结合LFP为基础的状态解码与尖峰为基础的运动解码可能是一个有价值的一步，实现BMI控制的多指神经假体执行灵巧的操作。
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.