Department of Biomedical Engineering, Duke University, Durham, NC 27708, USA.
Med Biol Eng Comput. 2009 Sep;47(9):955-66. doi: 10.1007/s11517-009-0451-2. Epub 2009 Feb 10.
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.
阈值处理是一种常用于植入式神经信号处理器的尖峰检测方法，因为它的计算简单。自动选择阈值的方法是可取的，特别是对于高通道计数数据采集系统。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一种计算简单的方法。我们对这种方法进行了分析，因为它通常应用于神经波形。使用四种不同的运算符来估计神经波形中的噪声水平，并为尖峰检测设置阈值。使用适合脑机接口应用的度量标准，为每个噪声测量值确定了最佳乘法器。研究人员使用这种形式的自动阈值选择或开发新的无监督方法可以从这里提出的优化框架中受益。
