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

H W Chen·C J Aine·E Best·D Ranken·R R Harrison·E R Flynn·C C Wood
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Abstract

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.

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Computer SimulationElectroencephalographyElectromagnetic FieldsEvoked Potentials, VisualHumansModels, NeurologicalNonlinear DynamicsRandom AllocationSignal Processing, Computer-AssistedJournal ArticleResearch Support, U.S. Gov't, Non-P.H.S.Research Support, U.S. Gov't, P.H.S.

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H W Chen, C J Aine, E Best, D Ranken, R R Harrison, E R Flynn, C C Wood. Nonlinear analysis of biological systems using short M-sequences and sparse-stimulation techniques.. https://doi.org/10.1007/bf02648113

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