Comparison of low-power biopotential processors for on-the-fly spike detection

Spike detection is a signal processing technique that can enable significant data rate reduction and resource savings in wireless brain monitoring. In these systems, energy-efficient spike detection algorithms are sought for enabling realtime signal processing while consuming low-power. As several spike detectors are based on ASIC, FPGA or low-power microcontroller unit (MCU), such algorithms must add little overhead to the entire system, while ensuring low error rate. In this paper, we present a comparative study of three different spike detection algorithms targeted toward implementation into low-power resource-constrained electronic systems. As practical validation, all candidate algorithms have been implemented on a popular low-power MCU and were fully characterized experimentally using previously recorded neural signals with different signal-to-noise ratios. A cost function based on detection rates, execution times, power consumption and resource utilization have been created and employed for comparing the detectors. The performances of all candidates are reported, and the best detector is identified. All candidate detectors present detection rate above 95% at high SNR, and above 78% for low SNR and can reduce the power consumption by up to 22.7%. This paper is the first to demonstrate the performances and hardware limitations of spike detectors on a low-power MCU system.