Nonlinear analysis of biological systems using short M-sequences and sparse-stimulation techniques

被引:3
作者
Chen, HW
Aine, CJ
Best, E
Ranken, D
Harrison, RR
Flynn, ER
Wood, CC
机构
[1] Biophysics Group, Los Alamos National Laboratory, Los Alamos, NM
[2] Biophysics Group, MS-D454, Los Alamos National Laboratory, Los Alamos
关键词
Volterra-Wiener approach; Volterra and Wiener kernels; random versus pseudorandom signals; anomalies of kernel estimation; inserted method; padded method; EEG and MEG studies;
D O I
10.1007/BF02648113
中图分类号
R318 [生物医学工程];
学科分类号
0831 ;
摘要
The m-sequence pseudorandom signal has been shown to be a more effective probing signal than traditional Gaussian white noise for studying nonlinear biological systems using cross-correlation techniques. The effectiveness is evidenced by the high signal-to-noise (S/N) ratio and speed of data acquisition. However, the ''anomalies'' that occur in the estimations of the cross-correlations represent an obstacle that prevents m-sequences from being more widely used for studying nonlinear systems. The sparse-stimulation method for measuring system kernels can help alleviate estimation errors caused by anomalies. In this paper, a ''padded sparse-stimulation'' method is evaluated, a modification of the ''inserted sparse-stimulation'' technique introduced by Sutter, along with a short m-sequence as a probing signal. Computer simulations show that both the ''padded'' and ''inserted'' methods can effectively eliminate the anomalies in the calculation of the second-order kernel, even when short m-sequences were used (length of 1023 for a binary m-sequence, and 728 for a ternary m-sequence). Preliminary experimental data from neuromagnetic studies of the human visual system are also presented, demonstrating that the system kernels can be measured with high signal-to-noise (S/N) ratios using short m-sequences.
引用
收藏
页码:513 / 536
页数:24
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