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

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  • Ariel Burman
  • Jordi Solé-Casals
  • Sergio E Lew

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.

Suggested Citation

  • Ariel Burman & Jordi Solé-Casals & Sergio E Lew, 2024. "Robust and memory-less median estimation for real-time spike detection," PLOS ONE, Public Library of Science, vol. 19(11), pages 1-14, November.
    • Handle: RePEc:plo:pone00:0308125
    DOI: 10.1371/journal.pone.0308125
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