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Exploring FPGA‐Based Lock‐In Techniques for Brain  Monitoring Applications

Laboratorio di Elettronica dei Sistemi Digitali Programmabili (ESDP Lab), Dipartimento di Energia, Ingegneria dell’informazione e Modelli matematici (DEIM), Università di Palermo, Viale delle Scienze (Bldg. 9), Palermo 90128, Italy
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Academic Editor: Mostafa Bassiouni
Electronics 2017, 6(1), 18; https://doi.org/10.3390/electronics6010018
Received: 31 December 2016 / Accepted: 27 February 2017 / Published: 2 March 2017
Functional near‐infrared spectroscopy (fNIRS) systems for e‐health applications usually suffer from 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 performance in such circumstances. In this work a digital LIA system, based on a Zynq® field programmable gate array (FPGA) has been designed and implemented, in an attempt to explore if this technique might improve fNIRS system performance. More broadly, FPGA‐based solution flexibility has been investigated, with particular emphasis applied to digital filter parameters, needed in the digital LIA, and its impact on the final signal detection and noise rejection capability has been evaluated. The realized architecture was a mixed solution between VHDL hardware modules and software modules, running within a microprocessor. Experimental results have shown the goodness of the proposed solutions and comparative details among different implementations will be detailed. Finally a key aspect taken into account throughout the design was its modularity, allowing an easy increase of the input channels while avoiding the growth of the design cost of the electronics system. View Full-Text
Keywords: 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 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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Giaconia, G.C.; Greco, G.; Mistretta, L.; Rizzo, R. Exploring FPGA‐Based Lock‐In Techniques for Brain  Monitoring Applications. Electronics 2017, 6, 18.

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