Preprint Article Version 1 This version is not peer-reviewed

Exploring FPGA Based Lock-in Techniques for Brain Monitoring Applications

Version 1 : Received: 13 February 2017 / Approved: 14 February 2017 / Online: 14 February 2017 (09:11:38 CET)

A peer-reviewed article of this Preprint also exists.

Giaconia, G.C.; Greco, G.; Mistretta, L.; Rizzo, R. Exploring FPGA‐Based Lock‐In Techniques for Brain  Monitoring Applications. Electronics 2017, 6, 18. Giaconia, G.C.; Greco, G.; Mistretta, L.; Rizzo, R. Exploring FPGA‐Based Lock‐In Techniques for Brain  Monitoring Applications. Electronics 2017, 6, 18.

Journal reference: Electronics 2017, 6, 18
DOI: 10.3390/electronics6010018

Abstract

Functional Near Infrared Spectroscopy (fNIRS) systems for e-health applications usually suffer of poor signal detection mainly due to a low end-to-end signal to noise ratio of the electronics chain. Lock-In Amplifiers (LIA) historically represent a powerful technique helping to improve performances in such circumstances. In this work it has been designed and implemented a digital LIA system, based on a Zynq® Field Programmable Gate Array (FPGA), trying to explore if this technique might improve fNIRS system performances. More broadly, FPGA based solution flexibility has been investigated, with particular emphasis applied to digital filter parameters, needed in the digital LIA, and it has been evaluated its impact on the final signal detection and noise rejection capability. The realized architecture was a mixed solution between VHDL hardware modules and software ones, running within a softcore microprocessor. Experimental results have shown the goodness of the proposed solutions and comparative details among different implementation will be detailed. Finally a key aspect taken into account throughout the design was its modularity, allowing an ease increase of the input channels while avoiding the growth of the design cost of the electronics system.

Subject Areas

Digital Lock-in Amplifier (DLIA); Field Programmable Gate Array (FPGA); Near Infrared Spectroscopy (NIRS); Hardware Description Language (HDL); Light Emitting Diode (LED); Silicon Photomultiplier (SiPM); Microprocessors

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