Spatiotemporal Templates for Distinguishing Neural Pathways in Peripheral Nerve Recordings

Koh, Ryan 1, 2 ; Nachman, Adrian 1, 3, 4 ; Zariffa, José 1, 2, 3

1. Institute of Biomaterials and Biomedical Engineering, University of Toronto; 2. Toronto Rehabilitation Institute – University Health Network, Toronto, ; 3. Edward S. Rogers Sr. Department of Electrical and Computer Engineering, University of Toronto; 4. Department of Mathematics, University of Toronto  

The peripheral nervous system can provide useful control signals in neuroprosthetic applications. However, it is very difficult to record selectively from different pathways within the peripheral nerves. Current techniques do not fully exploit the distinct “fingerprints” that action potentials make in both time and space as they propagate through the nerve. 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 create tailored matched filters for each of these pathways. Simulated measurements of compound action potentials propagating through the nerve in several different test cases at different noise levels were used to evaluate classification accuracy, percentage of missed spikes, and the ability to reconstruct the original firing rates of the different neural pathways of our algorithm, and existing techniques. 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 proposed algorithm, Bayesian spatial filters, and velocity selective recordings respectively. Preliminary results from in vivo recordings also suggest that our proposed algorithm can distinguish the activity of different pathways in the peripheral nerve. The proposed method shows that the spatiotemporal templates were able to provide more robust spike detection and reliable pathway discrimination than existing algorithms.