Please use this identifier to cite or link to this item: http://hdl.handle.net/1893/20649
Appears in Collections:Computing Science and Mathematics Journal Articles
Peer Review Status: Refereed
Title: Stream-based Hebbian eigenfilter for real-time neuronal spike discrimination
Authors: Yu, Bo
Mak, Terrence
Li, Xiangyu
Smith, Leslie
Sun, Yihe
Poon, Chi-Sang
Contact Email: l.s.smith@stir.ac.uk
Keywords: Brain-machine interface
Spike sorting
Hebbian eigenfilter
Hardware
Principal co
mponent analysis
Issue Date: Apr-2012
Publisher: BioMed Central
Citation: Yu B, Mak T, Li X, Smith L, Sun Y & Poon C (2012) Stream-based Hebbian eigenfilter for real-time neuronal spike discrimination, BioMedical Engineering Online, 11, Art. No.: 18.
Abstract: Background: Principal component analysis (PCA) has been widely employed for automatic neuronal spike sorting. Calculating principal components (PCs) is computationally expensive, and requires complex numerical operations and large memory resources. Substantial hardware resources are therefore needed for hardware implementations of PCA. General Hebbian algorithm (GHA) has been proposed for calculating PCs of neuronal spikes in our previous work, which eliminates the needs of computationally expensive covariance analysis and eigenvalue decomposition in conventional PCA algorithms. However, large memory resources are still inherently required for storing a large volume of aligned spikes for training PCs. The large size memory will consume large hardware resources and contribute significant power dissipation, which make GHA difficult to be implemented in portable or implantable multi-channel recording micro-systems. Method: In this paper, we present a new algorithm for PCA-based spike sorting based on GHA, namely stream-based Hebbian eigenfilter, which eliminates the inherent memory requirements of GHA while keeping the accuracy of spike sorting by utilizing the pseudo-stationarity of neuronal spikes. Because of the reduction of large hardware storage requirements, the proposed algorithm can lead to ultra-low hardware resources and power consumption of hardware implementations, which is critical for the future multi-channel micro-systems. Both clinical and synthetic neural recording data sets were employed for evaluating the accuracy of the stream-based Hebbian eigenfilter. The performance of spike sorting using stream-based eigenfilter and the computational complexity of the eigenfilter were rigorously evaluated and compared with conventional PCA algorithms. Field programmable logic arrays (FPGAs) were employed to implement the proposed algorithm, evaluate the hardware implementations and demonstrate the reduction in both power consumption and hardware memories achieved by the streaming computing Results and discussion: Results demonstrate that the stream-based eigenfilter can achieve the same accuracy and is 10 times more computationally efficient when compared with conventional PCA algorithms. Hardware evaluations show that 90.3% logic resources, 95.1% power consumption and 86.8% computing latency can be reduced by the stream-based eigenfilter when compared with PCA hardware. By utilizing the streaming method, 92% memory resources and 67% power consumption can be saved when compared with the direct implementation of GHA. Conclusion: Stream-based Hebbian eigenfilter presents a novel approach to enable real-time spike sorting with reduced computational complexity and hardware costs. This new design can be further utilized for multi-channel neuro-physiological experiments or chronic implants.
Type: Journal Article
URI: http://hdl.handle.net/1893/20649
DOI Link: http://dx.doi.org/10.1186/1475-925X-11-18
Rights: © 2012 Yu et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Affiliation: Tsinghua University
Newcastle University
Tsinghua University
Computing Science - CSM Dept
Tsinghua University
Massachusetts Institute of Technology

Files in This Item:
File Description SizeFormat 
BioMedical Engineering Online 2012.pdf702.78 kBAdobe PDFView/Open


This item is protected by original copyright



Items in the Repository are protected by copyright, with all rights reserved, unless otherwise indicated.

If you believe that any material held in STORRE infringes copyright, please contact library@stir.ac.uk providing details and we will remove the Work from public display in STORRE and investigate your claim.