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dc.contributor.authorYu, Boen_UK
dc.contributor.authorMak, Terrenceen_UK
dc.contributor.authorLi, Xiangyuen_UK
dc.contributor.authorSmith, Leslieen_UK
dc.contributor.authorSun, Yiheen_UK
dc.contributor.authorPoon, Chi-Sangen_UK
dc.description.abstractBackground: 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.en_UK
dc.publisherBioMed Centralen_UK
dc.relationYu 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.
dc.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 (, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.en_UK
dc.subjectBrain-machine interfaceen_UK
dc.subjectSpike sortingen_UK
dc.subjectHebbian eigenfilteren_UK
dc.subjectPrincipal coen_UK
dc.subjectmponent analysisen_UK
dc.titleStream-based Hebbian eigenfilter for real-time neuronal spike discriminationen_UK
dc.typeJournal Articleen_UK
dc.citation.jtitleBioMedical Engineering OnLineen_UK
dc.type.statusVoR - Version of Recorden_UK
dc.contributor.affiliationTsinghua Universityen_UK
dc.contributor.affiliationNewcastle Universityen_UK
dc.contributor.affiliationTsinghua Universityen_UK
dc.contributor.affiliationComputing Scienceen_UK
dc.contributor.affiliationTsinghua Universityen_UK
dc.contributor.affiliationMassachusetts Institute of Technologyen_UK
rioxxterms.typeJournal Article/Reviewen_UK
local.rioxx.authorYu, Bo|en_UK
local.rioxx.authorMak, Terrence|en_UK
local.rioxx.authorLi, Xiangyu|en_UK
local.rioxx.authorSmith, Leslie|0000-0002-3716-8013en_UK
local.rioxx.authorSun, Yihe|en_UK
local.rioxx.authorPoon, Chi-Sang|en_UK
local.rioxx.projectInternal Project|University of Stirling|
local.rioxx.filenameBioMedical Engineering Online 2012.pdfen_UK
Appears in Collections:Computing Science and Mathematics Journal Articles

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