A hybrid NEO-based spike detection algorithm for implantable brain-IC interface applications

Real time spike detection is the first critical step to develop spike-sorting for brain-IC interface applications. For implantable VLSI implementation, spike detection hardware must consume low power and at the same time ensure high true positive probability while having as few false alarms as possible. Currently, Nonlinear Energy Operator (NEO) and absolute thresholding are the two most widely used spike detection algorithms where NEO has a slightly better performance measured by areas under the receiver operating characteristic (ROC) curves. This paper revisits these two algorithms and quantitatively points out that NEO is in fact much better than absolute thresholding. We also propose a hybrid algorithm that offers similar accuracy as NEO but only requires 11% of power consumption.