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A Computationally Efficient Method for Incorporating Spike Waveform Information into Decoding Algorithms

机译:一种将尖峰波形信息纳入解码算法的计算有效方法

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摘要

Spike-based brain-computer interfaces (BCIs) have the potential to restore motor ability to people with paralysis and amputation, and have shown impressive performance in the lab. To transition BCI devices from the lab to the clinic, decoding must proceed automatically and in real time, which prohibits the use of algorithms that are computationally intensive or require manual tweaking. A common choice is to avoid spike sorting and treat the signal on each electrode as if it came from a single neuron, which is fast, easy, and therefore desirable for clinical use. But this approach ignores the kinematic information provided by individual neurons recorded on the same electrode. The contribution of this letter is a linear decoding model that extracts kinematic information from individual neurons without spike-sorting the electrode signals. The method relies on modeling sample averages of waveform features as functions of kinematics, which is automatic and requires minimal data storage and computation. In offline reconstruction of arm trajectories of a nonhuman primate performing reaching tasks, the proposed method performs as well as decoders based on expertly manually and automatically sorted spikes.
机译:基于Spike的脑机接口(BCI)具有恢复瘫痪和截肢患者运动能力的潜力,并且在实验室中表现出出色的性能。要将BCI设备从实验室转移到诊所,解码必须自动且实时进行,这会禁止使用计算量大或需要手动调整的算法。常见的选择是避免尖峰排序,并像对待单个电极上的信号一样对待每个电极上的信号,这是快速,容易的,因此对于临床使用是理想的。但是这种方法忽略了记录在同一电极上的单个神经元提供的运动学信息。这封信的贡献是一个线性解码模型,该模型从各个神经元中提取运动信息,而无需对电极信号进行尖峰排序。该方法依赖于将波形特征的样本平均值建模为运动学的函数,这是自动的,并且需要最少的数据存储和计算。在非人类的灵长类动物的手臂轨迹的离线重建中,执行到达任务时,所提出的方法在基于专家手动和自动分类的尖峰的情况下表现出色。

著录项

  • 来源
    《Neural computation》 |2015年第5期|1033-1050|共18页
  • 作者单位

    Department of Statistics, Carnegie Mellon University, and Center for the Neural Basis of Cognition, Pittsburgh, PA 15213, U.S.A. vventura@stat.cmu.edu;

  • 收录信息 美国《科学引文索引》(SCI);美国《化学文摘》(CA);
  • 原文格式 PDF
  • 正文语种 eng
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