In this section
@article{Zhang:2021:10.1016/j.jneumeth.2021.109103,
author = {Zhang, Z and Constandinou, T},
doi = {10.1016/j.jneumeth.2021.109103},
journal = {Journal of Neuroscience Methods},
title = {Adaptive spike detection and hardware optimization towards autonomous, high-channel-count BMIs},
url = {http://dx.doi.org/10.1016/j.jneumeth.2021.109103},
volume = {354},
year = {2021}
}
TY - JOUR
AB - BackgroundThe progress in microtechnology has enabled an exponential trend in the number of neurons that can be simultaneously recorded. The data bandwidth requirement is however increasing with channel count. The vast majority of experimental work involving electrophysiology stores the raw data and then processes this offline; to detect the underlying spike events. Emerging applications however require new methods for local, real-time processing.New MethodsWe have developed an adaptive, low complexity spike detection algorithm that combines three novel components for: (1) removing the local field potentials; (2) enhancing the signal-to-noise ratio; and (3) computing an adaptive threshold. The proposed algorithm has been optimised for hardware implementation (i.e. minimising computations, translating to a fixed-point implementation), and demonstrated on low-power embedded targets.Main resultsThe algorithm has been validated on both synthetic datasets and real recordings yielding a detection sensitivity of up to 90%. The initial hardware implementation using an off-the-shelf embedded platform demonstrated a memory requirement of less than 0.1kb ROM and 3kb program flash, consuming an average power of 130 μW.Comparison with Existing MethodsThe method presented has the advantages over other approaches, that it allows spike events to be robustly detected in real-time from neural activity in a completely autonomous way, without the need for any calibration, and can be implemented with low hardware resources.ConclusionThe proposed method can detect spikes effectively and adaptively. It alleviates the need for re-calibration, which is critical towards achieving a viable BMI, and more so with future ‘high bandwidth’ systems’ targeting 1000s of channels.
AU - Zhang,Z
AU - Constandinou,T
DO - 10.1016/j.jneumeth.2021.109103
PY - 2021///
SN - 0165-0270
TI - Adaptive spike detection and hardware optimization towards autonomous, high-channel-count BMIs
T2 - Journal of Neuroscience Methods
UR - http://dx.doi.org/10.1016/j.jneumeth.2021.109103
UR - http://hdl.handle.net/10044/1/87968
VL - 354
ER -