# Experimental Comparison of Hardware-Amenable Spike Detection Algorithms for iBMIs

## TL;DR

This study experimentally compares absolute threshold and non-linear energy operator spike detection algorithms in iBMIs, showing NEO's superior decoding performance in monkey experiments and advocating its adoption over AT.

## Contribution

First experimental comparison of spike detection algorithms in iBMIs involving two monkeys, demonstrating NEO's improved performance over AT.

## Key findings

- NEO increased decoding accuracy by ~5% in monkey A
- NEO increased decoding accuracy by ~2% in monkey B
- First reported experimental comparison of spike detection algorithms in iBMIs

## Abstract

This paper presents an experiment based comparison of absolute threshold (AT) and non-linear energy operator (NEO) spike detection algorithms in Intra-cortical Brain Machine Interfaces (iBMIs). Results show an average increase in decoding performance of approx. 5% in monkey A across 28 sessions recorded over 6 days and approx. 2% in monkey B across 35 sessions recorded over 8 days when using NEO over AT. To the best of our knowledge, this is the first ever reported comparison of spike detection algorithms in an iBMI experimental framework involving two monkeys. Based on the improvements observed in an experimental setting backed by previously reported improvements in simulation studies, we advocate switching from state of the art spike detection technique - AT to NEO.