In extracellular neural recording experiments, detecting neural spikes is an important step for reliable information decoding. A successful implementation in integrated circuits can achieve substantial data volume reduction, potentially enabling a wireless operation and closed-loop system. In this paper, we report a 16-channel neural spike detection chip based on a customized spike detection method named as exponential component-polynomial component (EC-PC) algorithm. This algorithm features a reliable prediction of spikes by applying a probability threshold. The chip takes raw data as input and outputs three data streams simultaneously: field potentials, band-pass filtered neural data, and spiking probability maps. The algorithm parameters are on-chip configured automatically based on input data, which avoids manual parameter tuning. The chip has been tested with both in vivo experiments for functional verification and bench-top experiments for quantitative performance assessment. The system has a total power consumption of 1.36 mW and occupies an area of 6.71 mm2 for 16 channels. When tested on synthesized datasets with spikes and noise segments extracted from in vivo preparations and scaled according to required precisions, the chip outperforms other detectors. A credit card sized prototype board is developed to provide power and data management through a USB port.
|Original language||English (US)|
|Number of pages||15|
|Journal||IEEE transactions on biomedical circuits and systems|
|State||Published - Feb 2016|
Bibliographical notePublisher Copyright:
© 2015 IEEE.
- EC-PC regression
- multichannel digital system
- neural signal processing
- unsupervised spike detection