Work

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

Year: 2024

Type: article

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.

Source: PLoS ONE

DOI: 10.1371/journal.pone.0308125

Authors: Ariel Burman, Jordi Solé-Casals, Sergio E. Lew

Institutions: Universidad de Buenos Aires, Universitat de Vic - Universitat Central de Catalunya, University of Cambridge

Language: English

Topic: Analog and Mixed-Signal Circuit Design

Subfield: Biomedical Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,290

Funders: Universidad de Buenos Aires, Secretaria de Ciencia y Tecnica, Universidad de Buenos Aires