Use of spatiotemporal templates for pathway discrimination in peripheral nerve recordings: a simulation study.

Abstract

Extraction of information from the peripheral nervous system can provide control signals in neuroprosthetic applications. However, the ability to selectively record from different pathways within peripheral nerves is limited. We investigated the integration of spatial and temporal information for pathway discrimination in peripheral nerves using measurements from a multi-contact nerve cuff electrode. Spatiotemporal templates were established for different neural pathways of interest, and used to obtain tailored matched filters for each of these pathways. Simulated measurements of compound action potentials propagating through the nerve in different test cases were used to evaluate classification accuracy, percentage of missed spikes, and ability to reconstruct the original firing rates of the neural pathways. The mean Pearson correlation coefficients between the original firing rates and estimated firing rates over all tests cases was found to be 0.832  ±  0.161, 0.421  ±  0.145, 0.481  ±  0.340 for our algorithm, Bayesian spatial filters, and velocity selective recordings respectively. The proposed method shows that the spatiotemporal templates were able to provide more robust spike detection and reliable pathway discrimination than these existing algorithms.