Design optimisation of front-end neural interfaces for spike sorting systems

This work investigates the impact of the analogue front-end design (pre-amplifier, filter and converter) on spike sorting performance in neural interfaces. By examining key design parameters including the signal-to-noise ratio, bandwidth, filter type/order, data converter resolution and sampling rate, their sensitivity to spike sorting accuracy is assessed. This is applied to commonly used spike sorting methods such as template matching, 2^nd derivative-features, and principle component analysis. The results reveal a near optimum set of parameters to increase performance given the hardware-constraints. Finally, the relative costs of these design parameters on resource efficiency (silicon area and power requirements) are quantified through reviewing the state-of-the-art.