Prior knowledge improves decoding of finger flexion from electrocorticographic signals.
Prior knowledge improves decoding of finger flexion from electrocorticographic signals.
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DOI：
10.3389/fnins.2011.00127
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发表时间：
2011
影响因子：
4.3
通讯作者：
Schalk G
中科院分区：
文献类型：
--
作者：
Wang Z;Ji Q;Miller KJ;Schalk G
脑机接口（BCI）使用大脑信号来传达用户的意图。一些 BCI 方法首先从大脑信号中解码运动的运动学参数，然后在没有运动的情况下继续使用这些信号，以允许用户控制输出。最近的结果表明，人类大脑表面的皮层电图（ECoG）记录可以提供有关运动学参数（例如手部速度或手指弯曲）的信息。这些研究中的解码方法通常采用经典的分类/回归算法，得出大脑信号和输出之间的线性映射。然而，它们通常只包含很少的关于目标运动参数的先验信息。在本文中，我们使用贝叶斯解码方法结合先验知识，并用它来解码 ECoG 信号中的手指弯曲。具体来说，我们利用控制手指弯曲的约束，并将这些约束合并到切换非参数动态系统（SNDS）的先验模型的构造、结构和概率函数中。给定传统线性回归方法产生的测量模型，我们使用结合先验模型和测量模型的后验估计来解码手指弯曲。我们的结果表明，与不结合先验知识的线性回归模型的应用相比，结合先验知识的贝叶斯解码模型的应用提高了解码性能。因此，本文提出的结果可能最终导致具有完全细粒度手指关节的神经控制手假肢。
Brain–computer interfaces (BCIs) use brain signals to convey a user’s intent. Some BCI approaches begin by decoding kinematic parameters of movements from brain signals, and then proceed to using these signals, in absence of movements, to allow a user to control an output. Recent results have shown that electrocorticographic (ECoG) recordings from the surface of the brain in humans can give information about kinematic parameters (e.g., hand velocity or finger flexion). The decoding approaches in these studies usually employed classical classification/regression algorithms that derive a linear mapping between brain signals and outputs. However, they typically only incorporate little prior information about the target movement parameter. In this paper, we incorporate prior knowledge using a Bayesian decoding method, and use it to decode finger flexion from ECoG signals. Specifically, we exploit the constraints that govern finger flexion and incorporate these constraints in the construction, structure, and the probabilistic functions of the prior model of a switched non-parametric dynamic system (SNDS). Given a measurement model resulting from a traditional linear regression method, we decoded finger flexion using posterior estimation that combined the prior and measurement models. Our results show that the application of the Bayesian decoding model, which incorporates prior knowledge, improves decoding performance compared to the application of a linear regression model, which does not incorporate prior knowledge. Thus, the results presented in this paper may ultimately lead to neurally controlled hand prostheses with full fine-grained finger articulation.
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影响因子：
4
作者：
Kubánek J;Miller KJ;Ojemann JG;Wolpaw JR;Schalk G
通讯作者：
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DOI：
10.1155/asp.2005.3113
发表时间：
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期刊：
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