Nahvi M J, Woody C D, Ungar R, Sharafat A R
Electroencephalogr Clin Neurophysiol. 1975 Feb;38(2):191-8. doi: 10.1016/0013-4694(75)90230-8.
A general mathematical formulation for predicting achievable levels of detection of neuroelectric signals in associated background noise is provided for the case where such data are obtainable from multiple recording loci. The formulation depends upon the signal-to-noise ratios and bandwidths of the incorporated data channels and on the degree of noise dependency between channels. The signals are assumed to relate to the same event such as the production of an incipient movement but need not have the same waveshape. The detection technique is based upon passage of the data through a series of optimum linear filters. The outputs of the filters can either be summated in analog fashion prior to making the detection decision, or their separate outputs and separate detection decisions can be treated combinatorially to determine a detection decision for the aggregate. The former method is superior to the latter for small numbers of data channels. The latter method may be preferable where variation in signal latency exists between channels. Incorporation of information from multiple channels with independent noise can result in significant improvement over detection signals from a single channel provided that the signal-to-noise level of each additional channel exceeds that of the aggregate divided by square rootK, K being the total number of added channels. However, the presence of noise dependency between channels may severly restrict the degree of imporvement realizable through the multiple channel detection operation, irrespective of the number of added cha-nels. The implication of this result on the possibility of using EEG signals predicting incipient movement to control the operation of a motor prosthesis is profound. Inter-channel noise dependency with correlation coefficienr filter method of detection levels required for prosthesis operation. Zero lag correlation coefficients between electrical recordings from separate cortical loci both in man a
针对可从多个记录位点获取神经电信号数据的情况，提供了一种用于预测在相关背景噪声中可实现的检测水平的通用数学公式。该公式取决于所合并数据通道的信噪比和带宽以及通道之间的噪声相关性程度。假定这些信号与同一事件相关，例如初期运动的产生，但波形不一定相同。检测技术基于数据通过一系列最优线性滤波器。滤波器的输出可以在做出检测决策之前以模拟方式求和，或者可以对它们的单独输出和单独的检测决策进行组合处理，以确定总体的检测决策。对于少量数据通道，前一种方法优于后一种方法。在通道之间存在信号潜伏期变化的情况下，后一种方法可能更可取。如果每个附加通道的信噪比超过总体信噪比除以平方根K（K为添加通道的总数），则合并来自具有独立噪声的多个通道的信息可导致比单通道检测信号有显著改善。然而，通道之间噪声相关性的存在可能会严重限制通过多通道检测操作可实现的改善程度，而与添加通道的数量无关。这一结果对使用脑电图信号预测初期运动以控制运动假体操作的可能性具有深远影响。通道间噪声相关性与相关系数滤波器方法对假体操作所需检测水平的影响。人及其他动物中来自不同皮质位点的电记录之间的零滞后相关系数…… （原文最后部分不完整）
