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A Real-Time Brain-Machine Interface Combining Motor Target and Trajectory Intent Using an Optimal Feedback Control Design

机译:利用最优反馈控制设计结合电机目标和轨迹意图的实时脑机接口

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摘要

Real-time brain-machine interfaces (BMI) have focused on either estimating the continuous movement trajectory or target intent. However, natural movement often incorporates both. Additionally, BMIs can be modeled as a feedback control system in which the subject modulates the neural activity to move the prosthetic device towards a desired target while receiving real-time sensory feedback of the state of the movement. We develop a novel real-time BMI using an optimal feedback control design that jointly estimates the movement target and trajectory of monkeys in two stages. First, the target is decoded from neural spiking activity before movement initiation. Second, the trajectory is decoded by combining the decoded target with the peri-movement spiking activity using an optimal feedback control design. This design exploits a recursive Bayesian decoder that uses an optimal feedback control model of the sensorimotor system to take into account the intended target location and the sensory feedback in its trajectory estimation from spiking activity. The real-time BMI processes the spiking activity directly using point process modeling. We implement the BMI in experiments consisting of an instructed-delay center-out task in which monkeys are presented with a target location on the screen during a delay period and then have to move a cursor to it without touching the incorrect targets. We show that the two-stage BMI performs more accurately than either stage alone. Correct target prediction can compensate for inaccurate trajectory estimation and vice versa. The optimal feedback control design also results in trajectories that are smoother and have lower estimation error. The two-stage decoder also performs better than linear regression approaches in offline cross-validation analyses. Our results demonstrate the advantage of a BMI design that jointly estimates the target and trajectory of movement and more closely mimics the sensorimotor control system.
机译:实时脑机接口(BMI)专注于估计连续运动轨迹或目标意图。但是,自然运动经常将两者结合在一起。另外,BMI可以建模为反馈控制系统,其中受试者调制神经活动以将假肢设备移向所需目标,同时接收运动状态的实时感官反馈。我们使用最佳反馈控制设计开发了一种新颖的实时BMI,该设计可在两个阶段共同估算猴子的运动目标和轨迹。首先,从运动开始之前的神经突刺活动中解码目标。其次,通过使用最佳反馈控制设计将解码后的目标与运动附近的尖峰活动相结合,对轨迹进行解码。该设计利用了递归贝叶斯解码器,该解码器使用了感觉运动系统的最佳反馈控制模型,以根据尖峰活动在其轨迹估计中考虑了预期目标位置和感觉反馈。实时BMI使用点过程建模直接处理尖峰活动。我们在由指令延迟居中任务组成的实验中实施BMI,在该任务中,猴子在延迟时间内在屏幕上显示了目标位置,然后必须将光标移动到该位置而不碰到不正确的目标。我们表明,两阶段BMI的表现比任何一个阶段都要准确。正确的目标预测可以补偿不正确的轨迹估计,反之亦然。最佳反馈控制设计还可以使轨迹更平滑并且估计误差较小。在离线交叉验证分析中,二级解码器的性能也优于线性回归方法。我们的结果证明了BMI设计的优势,该设计可共同估算运动的目标和轨迹,并更紧密地模拟感觉运动控制系统。

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