Optimizing the automatic selection of spike detection thresholds using a multiple of the noise level.
Optimizing the automatic selection of spike detection thresholds using a multiple of the noise level.
复制标题
使用多个噪声级别优化尖峰检测阈值的自动选择。
DOI：
10.1007/s11517-009-0451-2
复制
发表时间：
2009
影响因子：
3.2
通讯作者：
Wolf,PatrickD
中科院分区：
文献类型：
--
作者：
Rizk,Michael;Wolf,PatrickD
由于其计算简单性，双稳态保持是植入式神经信号处理器中经常使用的锋电位检测方法。需要一种自动选择阈值的方法，特别是对于高通道数数据采集系统。估计噪声水平并将阈值设置为该水平的倍数是自动选择阈值的计算简单的手段。我们提出了这种方法的分析，因为它通常适用于神经波形。四个不同的运营商被用来估计神经波形中的噪声水平，并设置阈值的尖峰检测。使用适合于脑机接口应用的度量为每个噪声测量确定最佳乘数。常用的均方根算子被认为是最有利的阈值设置。使用这种形式的自动阈值选择或开发新的无监督方法的研究人员可以从这里提出的优化框架中受益。
Thresholding is an often-used method of spike detection for implantable neural signal processors due to its computational simplicity. A means for automatically selecting the threshold is desirable, especially for high channel count data acquisition systems. Estimating the noise level and setting the threshold to a multiple of this level is a computationally simple means of automatically selecting a threshold. We present an analysis of this method as it is commonly applied to neural waveforms. Four different operators were used to estimate the noise level in neural waveforms and set thresholds for spike detection. An optimal multiplier was identified for each noise measure using a metric appropriate for a brain–machine interface application. The commonly used root-mean-square operator was found to be least advantageous for setting the threshold. Investigators using this form of automatic threshold selection or developing new unsupervised methods can benefit from the optimization framework presented here.