title: Linear methods for reducing EMG contamination in peripheral nerve motor decodes.
abstract: Signals recorded from the peripheral nervous system (PNS) with high channel count penetrating microelectrode arrays, such as the Utah Slanted Electrode Array (USEA), often have electromyographic (EMG) signals contaminating the neural signal. This common-mode signal source may prevent single neural units from successfully being detected, thus hindering motor decode algorithms. Reducing this EMG contamination may lead to more accurate motor decode performance. A virtual reference (VR), created by a weighted linear combination of signals from a subset of all available channels, can be used to reduce this EMG contamination. Four methods of determining individual channel weights and six different methods of selecting subsets of channels were investigated (24 different VR types in total). The methods of determining individual channel weights were equal weighting, regression-based weighting, and two different proximity-based weightings. The subsets of channels were selected by a radius-based criteria, such that a channel was included if it was within a particular radius of inclusion from the target channel. These six radii of inclusion were 1.5, 2.9, 3.2, 5, 8.4, and 12.8 electrode-distances; the 12.8 electrode radius includes all USEA electrodes. We found that application of a VR improves the detectability of neural events via increasing the SNR, but we found no statistically meaningful difference amongst the VR types we examined. The computational complexity of implementation varies with respect to the method of determining channel weights and the number of channels in a subset, but does not correlate with VR performance. Hence, we examined the computational costs of calculating and applying the VR and based on these criteria, we recommend an equal weighting method of assigning weights with a 3.2 electrode-distance radius of inclusion. Further, we found empirically that application of the recommended VR will require less than 1 ms for 33.3 ms of data from one USEA.
authors.forename: Zachary B
authors.initials: ZB
authors.lastname: Kagan
authors.forename: Suzanne
authors.initials: S
authors.lastname: Wendelken
authors.forename: David M
authors.initials: DM
authors.lastname: Page
authors.forename: Tyler
authors.initials: T
authors.lastname: Davis
authors.forename: Douglas T
authors.initials: DT
authors.lastname: Hutchinson
authors.forename: Gregory A
authors.initials: GA
authors.lastname: Clark
authors.forename: David J
authors.initials: DJ
authors.lastname: Warren
mesh_terms: D000465:Algorithms; D004576:Electromyography / Q000295:instrumentation / Q000379:methods*; D006801:Humans; D016014:Linear Models; D008839:Microelectrodes; D010525:Peripheral Nerves / Q000502:physiology*; D012815:Signal Processing, Computer-Assisted; D059629:Signal-To-Noise Ratio