Comparison of truncation and otsu-based thresholds for decoding subthreshold motor cortical activity in extracellular neural recordings

Abstract

This study examines two automated methods that enable the separation of extracellular neural recordings filtered in the action potential band into signal and subthreshold components known as ’noise’: Truncation Thresholds and Otsu-based Thresholds. While traditional methods generally focus on suprathreshold data, Truncation Thresholds and Otsu-based Thresholds methods offer a novel approach to behavioral decoding by utilizing subthreshold data. Previous studies have shown that the features (mean (µ) and standard deviation (σ)) of subthreshold data identified by Truncation Thresholds change with a rat’s behavior of pressing either the right or left pedal, and behavior can be decoded with up to 100% accuracy using these features. It has also been determined that Otsu-based Thresholds estimate the standard deviation of subthreshold components in simulated data more accurately than Truncation Thresholds. It implies that Otsu-based Thresholds method provides a more robust estimation by remaining unaffected by increasing neuronal firing rates.