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Identification of Multiple-Input Systems with Highly Coupled Inputs: Application to EMG Prediction from Multiple Intracortical Electrodes

dwestwic{at}ucalgary.ca Department of Electrical and Computer Engineering, University of Calgary, Calgary, Alberta, T2N 1N4, Canada.

Eric A. Pohlmeyer

e-pohlmeyer{at}northwestern.edu Department of Biomedical Engineering, Northwestern University, Evanston, IL 60208, U.S.A.

Sara A. Solla

solla{at}northwestern.edu Department of Physiology, Northwestern Medical School, Chicago, IL, 60611, and Department of Physics and Astronomy, Northwestern University, Evanston, IL 60208, U.S.A.

Lee E. Miller

lm{at}northwestern.edu Department of Physiology, Northwestern Medical School, Chicago, IL 60611, U.S.A.

Eric J. Perreault

e-perreault{at}northwestern.edu Department of Physical Medicine and Rehabilitation, Northwestern University Medical School, Chicago, IL 60611, U.S.A.

A robust identification algorithm has been developed for linear, time-invariant, multiple-input single-output systems, with an emphasis on how this algorithm can be used to estimate the dynamic relationship between a set of neural recordings and related physiological signals. The identification algorithm provides a decomposition of the system output such that each component is uniquely attributable to a specific input signal, and then reduces the complexity of the estimation problem by discarding those input signals that are deemed to be insignificant. Numerical difficulties due to limited input bandwidth and correlations among the inputs are addressed using a robust estimation technique based on singular value decomposition. The algorithm has been evaluated on both simulated and experimental data. The latter involved estimating the relationship between up to 40 simultaneously recorded motor cortical signals and peripheral electromyograms (EMGs) from four upper limb muscles in a freely moving primate. The algorithm performed well in both cases: it provided reliable estimates of the system output and significantly reduced the number of inputs needed for output prediction. For example, although physiological recordings from up to 40 different neuronal signals were available, the input selection algorithm reduced this to 10 neuronal signals that made significant contributions to the recorded EMGs.