Citation

BibTex format

@article{Zhang:2022:10.1101/2022.01.25.477685,
author = {Zhang, Z and Constandinou, TG},
doi = {10.1101/2022.01.25.477685},
title = {Selecting an effective amplitude threshold for neural spike detection},
url = {http://dx.doi.org/10.1101/2022.01.25.477685},
year = {2022}
}

RIS format (EndNote, RefMan)

TY  - JOUR
AB - <jats:title>Abstract</jats:title><jats:p>This paper assesses and challenges whether commonly used methods for defining amplitude thresholds for spike detection are optimal. This is achieved through empirical testing of single amplitude thresholds across multiple recordings of varying SNR levels. Our results suggest that the most widely used noise-statistics-driven threshold can suffer from parameter deviation in different noise levels. The spike-noise-driven threshold can be an ideal approach to set the threshold for spike detection, which suffers less from the parameter deviation and is robust to sub-optimal settings.</jats:p>
AU - Zhang,Z
AU - Constandinou,TG
DO - 10.1101/2022.01.25.477685
PY - 2022///
TI - Selecting an effective amplitude threshold for neural spike detection
UR - http://dx.doi.org/10.1101/2022.01.25.477685
ER -

Contact us

Centre for Bio-Inspired Technology
Imperial College London
Bessemer Building
South Kensington
SW7 2AZ, UK

Tel: +44 (0)207 594 0701
Fax: +44 (0)207 594 0704

E-mail: bioinspired@imperial.ac.uk