Paper Info
Title
Blind parallel interrogation of ultrasonic neural dust motes based on canonical polyadic decomposition: A simulation study.
Abstract
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, NI) could allow to chronically record electro-physiological signals in the brain cortex at a micro-scale pitch. The free-floating NDMs arc 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 NI) 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 NI) 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 CPI)-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.
Year
Venue
Field
2017
European Signal Processing Conference
Ultrasonic sensor,Signal processing,Telecommunications,Wireless,Computer science,Sampling (signal processing),Backscatter,Communication channel,Beam steering,Acoustics,Grid
DocType
ISSN
Citations 
Conference
2076-1465
0
PageRank 
References 
Authors
0.34
2
6
Name
Order
Citations
PageRank
Alexander Bertrand160748.80
Dongjin Seo2122.06
Jose M. Carmena324735.30
Michel M Maharbiz48820.12
Elad Alon5912121.97
Jan M. Rabaey647961049.96