Bayesian spatial filters for source signal extraction: a study in the peripheral nerve

The ability to extract physiological source signals to control various prosthetics offer tremendous therapeutic potential to improve the quality of life for patients suffering from motor disabilities. Regardless of the modality, recordings of physiological source signals are contaminated with noise and interference along with crosstalk between the sources. These impediments render the task of isolating potential physiological source signals for control difficult. In this paper, a novel Bayesian Source Filter for signal Extraction (BSFE) algorithm for extracting physiological source signals for control is presented. The BSFE algorithm is based on the source localization method Champagne and constructs spatial filters using Bayesian methods that simultaneously maximize the signal to noise ratio of the recovered source signal of interest while minimizing crosstalk interference between sources. When evaluated over peripheral nerve recordings obtained in vivo, the algorithm achieved the highest signal to noise interference ratio ( 7.00 ±3.45 dB) amongst the group of methodologies compared with average correlation between the extracted source signal and the original source signal R = 0.93. The results support the efficacy of the BSFE algorithm for extracting source signals from the peripheral nerve.