title: Nonlinear analysis of biological systems using short M-sequences and sparse-stimulation techniques.
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.
authors.affiliation: Biophysics Group, Los Alamos National Laboratory, NM 87545, USA.
authors.forename: H W
authors.initials: HW
authors.lastname: Chen
authors.forename: C J
authors.initials: CJ
authors.lastname: Aine
authors.forename: E
authors.initials: E
authors.lastname: Best
authors.forename: D
authors.initials: D
authors.lastname: Ranken
authors.forename: R R
authors.initials: RR
authors.lastname: Harrison
authors.forename: E R
authors.initials: ER
authors.lastname: Flynn
authors.forename: C C
authors.initials: CC
authors.lastname: Wood
mesh_terms: D003198:Computer Simulation; D004569:Electroencephalography; D004574:Electromagnetic Fields; D005074:Evoked Potentials, Visual / Q000502:physiology; D006801:Humans; D008959:Models, Neurological; D017711:Nonlinear Dynamics; D011897:Random Allocation; D012815:Signal Processing, Computer-Assisted