Robust and Memoryless Median Estimation for Real-Time Spike Detection

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Robust and Memoryless Median Estimation for Real-Time Spike Detection

Authors

Burman, A.; Sole-Casals, J.; Lew, S. E.

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.

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