Abstract
This article presents a comprehensive survey of literature on the compressed sensing (CS) of neurophysiology signals. CS is a promising technique to achieve high-fidelity, low-rate, and hardware-efficient neural signal compression tasks for wireless streaming of massively parallel neural recording channels in next-generation neural interface technologies. The main objective is to provide a timely retrospective on applying the CS theory to the extracellular brain signals in the past decade. We will present a comprehensive review on the CS-based neural recording system architecture, the CS encoder hardware exploration and implementation, the sparse representation of neural signals, and the signal reconstruction algorithms. Deep learning-based CS methods are also discussed and compared with the traditional CS-based approaches. We will also extend our discussion to cover the technical challenges and prospects in this emerging field.
Highlights
Extracellular neural recording has been established for decades for monitoring the neuronal ensemble activities with better temporal resolution (Hubel, 1957; Buzsáki, 2004; Stevenson and Kording, 2011)
Compared with other neural activity monitoring approaches, extracellular neural recording offers a broad recording spectrum of electrophysiology signals generated by the living brains, which spans from the slow-varying local field potentials (LFPs) to the transient spiking activities (Buzsáki et al, 2012)
APs play a key role in neuron-to-neuron communication across the entire nervous system and have been widely studied for their functional representation in neural coding, while LFPs reflect the highly dynamic information flows beyond the reach of observing spiking activities from a few neurons, and have been studied for motor decoding in brain–machine interfaces (Andersen et al, 2004), sleep states (Vyazovskiy et al, 2011), sensory processing (Haslinger et al, 2006) as well as higher cognitive processes such as attention, memory, and perception
Summary
Extracellular neural recording has been established for decades for monitoring the neuronal ensemble activities with better temporal resolution (Hubel, 1957; Buzsáki, 2004; Stevenson and Kording, 2011). Compared with other neural activity monitoring approaches, extracellular neural recording offers a broad recording spectrum of electrophysiology signals generated by the living brains, which spans from the slow-varying local field potentials (LFPs) to the transient spiking activities (action potentials [APs]) (Buzsáki et al, 2012). Both neural signal modalities carry essential brain processing information and are crucial to the understanding of brain functions. Equation (1) is underdetermined, i.e., m < n, and the ratio n/m is called the compression ratio (CR)
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