Recordings made directly from the nervous system are a key tool in experimental electrophysiology and the development of bioelectronic medicines. Analysis of these recordings involves the identification of signals from individual neurons, a process known as spike sorting. A critical and limiting feature of spike sorting is the need to align individual spikes in time. However, electrophysiological recordings are made in extremely noisy environments that seriously limit the performance of the spike-alignment process. We present a new centroid-based method and demonstrate its effectiveness using deterministic models of nerve signals. We show that spike alignment in the presence of noise is possible with a 30 dB reduction in minimum SNR compared to conventional methods. We present a mathematical analysis of the centroid method, characterising its fundamental operation and performance. Further, we show that the centroid method lends itself particularly well to hardware realisation and we present results from a low-power implementation that operates on an FPGA, consuming 10 times less power than conventional techniques - an important property for implanted devices. Our centroid method enables the accurate alignment of spikes in sub-0 dB SNR recordings and has the potential to enable the analysis of spikes in a wider range of environments than has been previously possible. Our method thus has the potential to influence significantly the design of electrophysiological recording systems in the future.
|Number of pages||10|
|Journal||IEEE Transactions on Neural Systems and Rehabilitation Engineering|
|Early online date||16 Jun 2017|
|Publication status||Published - 30 Nov 2017|
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- Department of Electronic & Electrical Engineering - Lecturer
- Centre for Biosensors, Bioelectronics and Biodevices (C3Bio)
- UKRI CDT in Accountable, Responsible and Transparent AI
- Centre for Autonomous Robotics (CENTAUR)
- Electronics Materials, Circuits & Systems Research Unit (EMaCS)
Person: Research & Teaching, Researcher