State-space decoding of goal-directed movements
State-space decoding of goal-directed movements
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DOI：
10.1109/msp.2008.4408444
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发表时间：
2008-01-01
影响因子：
14.9
通讯作者：
Paninski, Liam
中科院分区：
文献类型：
--
作者：
Kulkarni, Jayant E.;Paninski, Liam
贝叶斯推理方法在预测神经义肢的手部运动轨迹方面具有很大的前景。这种概率方法的准确性可以通过纳入有意义的先验来提高，从而适当地限制系统可以达到的可能状态的空间。在这项工作中，我们进行了回顾和扩展。构造包含预定运动目标的先验信息的到达轨迹的方法。为了计算的可追溯性，我们将手臂运动建模为一个由高斯噪声驱动的线性动力系统，该系统以该端点信息为条件。这些假设虽然在生物力学上是不现实的，但却产生了一个先验的模型手臂路径，它与自然手臂轨迹有许多共同的特征。此外，在这个模型公式中，我们可以计算预测的手臂位置，给定同时观察到的神经数据，使用从卡尔曼滤波理论中熟悉的标准正向向后计算。在这里，我们回顾了用于计算这种到达轨迹的早期递归方法，并提出了一种新的非递归方法，其计算可以在大部分情况下进行分析，从而显著提高推断轨迹的准确性，同时施加非常小的计算负担。最后，我们讨论了我们方法的扩展，包括在不同时间合并多个目标观测，以及多个可能的目标位置。
Bayesian inference methods hold great promise for the prediction of hand-movement trajectories in neural prosthetic devices. The accuracy of such probabilistic methods can be improved by incorporating meaningful priors, thereby appropriately constraining the space of possible states that the system can attain. In this work we review and extend. methods for constructing reach trajectories that incorporate prior information of the intended movement target. For computational tractability, we model arm motion as a linear dynamical system driven by Gaussian noise, conditioned on this end-point information. These assumptions, while biomechanically unrealistic, give rise to a priori model arm-paths that share many of the characteristics of natural arm trajectories. Moreover, in this model formulation we may compute the predicted arm position, given simultaneously observed neural data, using standard forward-backward computations familiar from the theory of the Kalman filter. Here we review an earlier recursive approach for computing such reach trajectories and present a new nonrecursive approach, with computations that may be performed analytically for the most part, leading to a significant gain in the accuracy of the inferred trajectory while imposing a very small computational burden. Finally, we discuss extensions of our approach, including the incorporation of multiple target observations at different times, and multiple possible target locations.