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

Ariel Burman, Jordi Solé-Casals and Sergio E Lew

PLOS ONE, 2024, vol. 19, issue 11, 1-14

Abstract: 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.

Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0308125

DOI: 10.1371/journal.pone.0308125

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