Robust and memory-less median estimation for real-time spike detection.

Autor: Burman A; Instituto de Ingeniería Biomédica, Universidad de Buenos Aires, Buenos Aries, Argentina., Solé-Casals J; Data and Signal Processing Group, University of Vic-Central University of Catalonia, Vic, Spain.; Department of Psychiatry, University of Cambridge, Cambridge, United Kingdom., Lew SE; Instituto de Ingeniería Biomédica, Universidad de Buenos Aires, Buenos Aries, Argentina.
Jazyk: angličtina
Zdroj: PloS one [PLoS One] 2024 Nov 26; Vol. 19 (11), pp. e0308125. Date of Electronic Publication: 2024 Nov 26 (Print Publication: 2024).
DOI: 10.1371/journal.pone.0308125
Abstrakt: We propose a novel 1-D median estimator specifically designed for the online detection of threshold-crossing signals, such as spikes in extracellular neural recordings. Compared to state-of-the-art algorithms, our method reduces estimator variance by up to eight times for a given buffer length. Likewise, for a given estimator variance, it requires a buffer length that is up to eight times smaller. This results in three significant advantages: the footprint area decreases by more than eight times, leading to reduced power consumption and a faster response to non-stationary signals.
Competing Interests: The authors have declared that no competing interests exist.
(Copyright: © 2024 Burman et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)
Databáze: MEDLINE
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