Inducing Dynamic Group Sparsity on Vagus Nerve Recordings

As recording electrodes improve with higher spatial resolution, the large amount of incoming data is becoming difficult to handle on low-resource medical technologies, such as implantable devices. Processing each data sample is costly in terms of energy, memory allocation, bandwidth transmission and more. Accurate neural compression has thus become important in reducing the amount of data that needs to be processed, whether to an implantable device or a distant computer. Compression algorithms, such as EI balance networks, have been successfully implemented, but rely on the physical structure of neurons to have many connections, such as in the cortex. This structure does not exist in the peripheral nervous system and so cannot be accurately deployed. In this paper, we employ dynamic group sparsity (DGS) on two different nerve recording datasets to demonstrate intelligent compression schemes while retaining signal representation. DGS does not require structural connections, but instead assumes that neurons fire in groups. We assume that there is structure among neuron groups that fire sparsely, both spatially and temporally. We show that under certain conditions, DGS achieves 5x compression while retaining 99.2% signal integrity. We then compare these results to typical matching pursuit algorithms, such as OMP and CoSaMP, and conclude with future implementations of DGS in post-acquisition nerve recordings.