Abstract
Real-Time Neural Signal Sensing and Spike Sorting System Using Modified Zero-Crossing Feature with Highly Efficient Data Computation and Transmission
Highlights
IntroductionReal-time neural spike sorting provides substantial benefits in closed-loop neural recordings and stimulating systems because feedback on neuronal activities can be obtained in real time.[1,2,3,4]In addition, real-time spike sorting is necessary to develop a high-performance brain-machine interface (BMI) where real-time movement prediction is used to control prosthetic devices.[5,6,7,8]Recently developed multi-channel implantable neural recording devices require real-time neural spike sorting.[9]Recently, a CMOS technology-based high-density micro-electrode array (HDMEA) composed of more than four thousand recording electrodes was used to detect complicated neuronal activities.[10,11,12] Because of the significantly increased number of recording electrodes, the amount of recorded data was dramatically increased
Traditional spike sorting algorithms, such as principal component analysis, template matching, and wavelet transform, are inappropriate to implement highly efficient real-time spike sorting because they require heavy computational effort and offline training to extract spike features.[13,14,15,16,17] In a previous study, a zero-crossing feature (ZCF) was proposed as the computationally efficient spike classification element with a significantly reduced data set size.[18,19] the original ZCF extraction algorithm can cause inaccurate sorting results because the number of samples in a temporal window for a spike is predetermined and fixed, regardless of the duration of the spike.[18]
A real-time neural signal sensing and spike sorting system using a modified ZCF is described in this paper
Summary
Real-time neural spike sorting provides substantial benefits in closed-loop neural recordings and stimulating systems because feedback on neuronal activities can be obtained in real time.[1,2,3,4]In addition, real-time spike sorting is necessary to develop a high-performance brain-machine interface (BMI) where real-time movement prediction is used to control prosthetic devices.[5,6,7,8]Recently developed multi-channel implantable neural recording devices require real-time neural spike sorting.[9]Recently, a CMOS technology-based high-density micro-electrode array (HDMEA) composed of more than four thousand recording electrodes was used to detect complicated neuronal activities.[10,11,12] Because of the significantly increased number of recording electrodes, the amount of recorded data was dramatically increased. Developed multi-channel implantable neural recording devices require real-time neural spike sorting.[9]. Traditional spike sorting algorithms, such as principal component analysis, template matching, and wavelet transform, are inappropriate to implement highly efficient real-time spike sorting because they require heavy computational effort and offline training to extract spike features.[13,14,15,16,17] In a previous study, a zero-crossing feature (ZCF) was proposed as the computationally efficient spike classification element with a significantly reduced data set size.[18,19] the original ZCF extraction algorithm can cause inaccurate sorting results because the number of samples in a temporal window for a spike is predetermined and fixed, regardless of the duration of the spike.[18]
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