Towards Next Generation Neural Interfaces: Optimizing Power, Bandwidth and Data Quality

Abstract

In this paper, we review the state-of-the-art in neural interface recording architectures. Through this we identify schemes which show the trade-off between data information quality (lossiness), computation (i.e. power and area requirements) and the number of channels. These trade-offs are then extended by considering the front-end amplifier bandwidth to also be a variable. We therefore explore the possibility of band-limiting the spectral content of recorded neural signals (to save power) and investigate the effect this has on subsequent processing (spike detection accuracy). We identify the spike detection method most robust to such signals, optimize the threshold levels and modify this to exploit such a strategy.