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


METHODS
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.


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.
Centre for Systems Neuroscience, University of Leicester, 9 Salisbury Road, LE1 7QR, United Kingdom. Electronic address: jmna1@le.ac.uk.
Joaquin
J
Navajas
Centre for Bio-Inspired Technology, Department of Electrical and Electronic Engineering, Imperial College London, SW7 2AZ, United Kingdom.
Deren Y
DY
Barsakcioglu
Centre for Bio-Inspired Technology, Department of Electrical and Electronic Engineering, Imperial College London, SW7 2AZ, United Kingdom.
Amir
A
Eftekhar
Institute of Neuroscience, Newcastle University, Newcastle-upon-Tyne NE2 4HH, United Kingdom.
Andrew
A
Jackson
Centre for Bio-Inspired Technology, Department of Electrical and Electronic Engineering, Imperial College London, SW7 2AZ, United Kingdom.
Timothy G
TG
Constandinou
Centre for Systems Neuroscience, University of Leicester, 9 Salisbury Road, LE1 7QR, United Kingdom.
Rodrigo
R
Quian Quiroga
D000200:Action Potentials; D003198:Computer Simulation; D004567:Electrodes, Implanted; D004594:Electrophysiology / Q000295:instrumentation* / Q000379:methods*; D004827:Epilepsy / Q000503:physiopathology; D005110:Extracellular Space / Q000502:physiology; D005240:Feasibility Studies; D006801:Humans; D008959:Models, Neurological; D009474:Neurons / Q000502:physiology*; D010363:Pattern Recognition, Automated; D019736:Prostheses and Implants; D012815:Signal Processing, Computer-Assisted; D013702:Temporal Lobe / Q000502:physiology / Q000503:physiopathology; D013997:Time Factors; D059015:Wireless Technology
BMIs; Extracellular recordings; On-chip; On-line; Real-time; Spike sorting; Template matching