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Minimum requirements for accurate and efficient real-time on-chip spike sorting

Lookup NU author(s): Professor Andrew Jackson

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Abstract

Background: Extracellular recordings are performed by inserting electrodes in the brain, relaying the signals to external power-demanding devices, where spikes are detected and sorted in order to identify the firing activity of different putative neurons. A main caveat of these recordings is the necessity of wires passing through the scalp and skin in order to connect intracortical electrodes to external amplifiers. The aim of this paper is to evaluate the feasibility of an implantable platform (i.e. a chip) with the capability to wirelessly transmit the neural signals and perform real-time on-site spike sorting.New method: We computationally modelled a two-stage implementation for online, robust, and efficient spike sorting. In the first stage, spikes are detected on-chip and streamed to an external computer where mean templates are created and sent back to the chip. In the second stage, spikes are sorted in real-time through template matching.Results: We evaluated this procedure using realistic simulations of extracellular recordings and describe a set of specifications that optimise performance while keeping to a minimum the signal requirements and the complexity of the calculations. Comparison with existing methods: A key bottleneck for the development of long-term BMIs is to find an inexpensive method for real-time spike sorting. Here, we simulated a solution to this problem that uses both offline and online processing of the data.Conclusions: Hardware implementations of this method therefore enable low-power long-term wireless transmission of multiple site extracellular recordings, with application to wireless BMIs or closed-loop stimulation designs. (C) 2014 Elsevier B.V. All rights reserved.

Publication metadata

Author(s): Navajas J, Barsakcioglu DY, Eftekhar A, Jackson A, Constandinou TG, Quiroga RQ

Publication type: Article

Publication status: Published

Journal: Journal of Neuroscience Methods

Year: 2014

Volume: 230

Pages: 51-64

Print publication date: 15/06/2014

Online publication date: 24/04/2014

Acceptance date: 14/04/2014

ISSN (print): 0165-0270

ISSN (electronic): 1872-678X

Publisher: Elsevier BV

URL: http://dx.doi.org/10.1016/j.jneumeth.2014.04.018

DOI: 10.1016/j.jneumeth.2014.04.018

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