Stream-based Hebbian eigenfilter for real-time neuronal spike discrimination

Abstract

BackgroundPrincipal 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.MethodIn 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 computingResults and discussionResults 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.ConclusionStream-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.

Identification of a self-paced hitting task in freely moving rats based on adaptive spike detection from multi-unit M1 cortical signals

“…The spike sorting typically add processing time that is proportional to the number of detected neurons and number of recording channels ( Ventura, 2008 ; Herzfeld and Beardsley, 2010 ). Furthermore, the number and characteristics of the detected spikes may be subjected to daily change due to small changes at the neural interface (e.g., micro-motions of the brain or fibrosis formation around the electrodes) or even cell death ( Lewicki, 1998 ; Yu et al, 2012 ).…”

Analysis of spike waves in epilepsy using Hilbert-Huang transform

“…10 The wafi of the IMFs for EEG signal without spike wave, and spike I, II, and III sample waves Fig. 11 The wami of the IMFs for EEG signal without spike wave, and spike I, II, and III sample waves Spike I, II, and III waves of theγbands in the interval of 0-0.096 s were higher than that of the energy of RF to its referred total energy for the EEG signal without spike waves of theγband in the interval of 0-0.096 s. The frequency with the most power was determined using the weighted average frequency of the iIMF waf i , and the weighted average magnitude of the iIMF wam i [31][32][33] (http://www.vis.caltech.edu/%7Erodri/Wave_clus/Wave_ clus_home.htm). The weighted average frequency of the ith IMF is defined as follows:…”

“…Tan et al [30] applied a small three-dimensional, localized disturbance to characterize a spike-type stall inception. Yu et al [31] proposed a Hebbian algorithm for designing the crucial constitutions of neuronal spikes in the preceding effort. The need for computationally costly covariance analysis and eigenvalue decomposition was disregarded in the proposed constituting algorithms.…”

QSpike tools: a generic framework for parallel batch preprocessing of extracellular neuronal signals recorded by substrate microelectrode arrays

“…From Neuroinformatics perspectives, several open-source software toolboxes have been developed and released to the community over the years, addressing those issues and ultimately aimed at making electrophysiological data handling and analysis easier, faster, interactive, and more user friendly (Egert et al, 2002 ; Quiroga et al, 2004 ; Vato et al, 2004 ; Bonomini et al, 2005 ; Wagenaar et al, 2005 ; Morup et al, 2007 ; Cui et al, 2008 ; Huang et al, 2008 ; Magri et al, 2009 ; Novellino et al, 2009 ; Bologna et al, 2010 ; Abdoun et al, 2011 ; Kwon et al, 2012 ; Mahmud et al, 2012 ; Just et al, 2013 ). Hardware based techniques have been also made available to the community to perform spike detection and sorting (Yu et al, 2012 ; Hwang et al, 2013 ). Nonetheless, signal processing and data analysis remain intensive, even though modern personal computing power increased dramatically and costs steadily decreased.…”