Real-time FPGA-based multichannel spike sorting using Hebbian eigenfilters
Name
journal_v17.pdf
Description
IEEE Journal article
Size
6.92 MB
Format
Adobe PDF
Checksum (MD5)
54778fce0d453d4849db30df2c24af5e
Author(s)
Yu, Bo
Mak, Terrence
Li, Xiangyu
Xia, Fei
Yakovlev, Alex
Sun, Yihe
Poon, Chi-Sang
Date Issued
December 2011
Journal
IEEE Journal on Emerging and Selected Topics in Circuits and Systems
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
Yu, Bo et al. “Real-Time FPGA-Based Multichannel Spike Sorting Using Hebbian Eigenfilters.” IEEE Journal on Emerging and Selected Topics in Circuits and Systems 1.4 (2011): 502-515. Web. 16 Feb. 2012. © 2012 Institute of Electrical and Electronics Engineers
Version
Author's final manuscript
Abstract
Real-time multichannel neuronal signal recording has spawned broad applications in neuro-prostheses and neuro-rehabilitation. Detecting and discriminating neuronal spikes from multiple spike trains in real-time require significant computational efforts and present major challenges for hardware design in terms of hardware area and power consumption. This paper presents a Hebbian eigenfilter spike sorting algorithm, in which principal components analysis (PCA) is conducted through Hebbian learning. The eigenfilter eliminates the need of computationally expensive covariance analysis and eigenvalue decomposition in traditional PCA algorithms and, most importantly, is amenable to low cost hardware implementation. Scalable and efficient hardware architectures for real-time multichannel spike sorting are also presented. In addition, folding techniques for hardware sharing are proposed for better utilization of computing resources among multiple channels. The throughput, accuracy and power consumption of our Hebbian eigenfilter are thoroughly evaluated through synthetic and real spike trains. The proposed Hebbian eigenfilter technique enables real-time multichannel spike sorting, and leads the way towards the next generation of motor and cognitive neuro-prosthetic devices.
MIT Department
Harvard University--MIT Division of Health Sciences and Technology
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike 3.0
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1109/JETCAS.2012.2183430
