Robust and Memoryless Median Estimation for Real-Time Spike Detection
Ariel Burman, Jordi Solé‐Casals, Sergio E. Lew · bioRxiv (Cold Spring Harbor Laboratory) · 2024
Abstract We propose a novel moving median estimator specifically designed for online detection of threshold crossings in multi-channel signals, such as extracellular neural recordings. This estimator offers two key advantages: a reduced sensitivity to outliers and the elimination of memory requirements for storing arrival times. Furthermore, its design facilitates parallel implementation on FPGAs, making it ideal for real-time processing of multi-channel recordings.