Blind parallel interrogation of ultrasonic neural dust motes based on canonical polyadic decomposition: A simulation study

Neural dust (ND) is a wireless ultrasonic backscatter system for communicating with implanted sensor devices, referred to as ND motes (NDMs). Due to its scalability, ND could allow to chronically record electro-physiological signals in the brain cortex at a micro-scale pitch. The free-floating NDMs are read out by an array of ultrasonic (US) transducers through passive backscattering, by sequentially steering a US beam to the target NDM. In order to perform such beam steering, the NDM positions or the channels between the NDMs and the US transducers have to be estimated, which is a non-trivial task. Furthermore, such a sequential beam steering approach is too slow to sample a dense ND grid with a sufficiently high sampling rate. In this paper, we propose a new ND interrogation scheme which is fast enough to completely sample the entire ND grid, and which does not need any information on the NDM positions or the per-NDM channel characteristics. For each sample time, the US transducers transmit only a few grid-wide US beams to the entire ND grid, in which case the reflected beams will consist of mixtures of multiple NDM signals. We arrange the demodulated backscattered signals in a 3-way tensor, and then use a canonical polyadic decomposition (CPD) to blindly estimate the neural signals from each underlying NDM. Based on a validated simulation model, we demonstrate that this new CPD-based interrogation scheme allows to reconstruct the neural signals from the entire ND grid with a sufficiently high accuracy, even at relatively low SNR regimes.