Exploring FPGA‐Based Lock‐In Techniques for Brain Monitoring Applications
AbstractFunctional 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
Scifeed alert for new publicationsNever miss any articles matching your research from any publisher
- Get alerts for new papers matching your research
- Find out the new papers from selected authors
- Updated daily for 49'000+ journals and 6000+ publishers
- Define your Scifeed now
Giaconia, G.C.; Greco, G.; Mistretta, L.; Rizzo, R. Exploring FPGA‐Based Lock‐In Techniques for Brain Monitoring Applications. Electronics 2017, 6, 18.
Giaconia GC, Greco G, Mistretta L, Rizzo R. Exploring FPGA‐Based Lock‐In Techniques for Brain Monitoring Applications. Electronics. 2017; 6(1):18.Chicago/Turabian Style
Giaconia, Giuseppe C.; Greco, Giuseppe; Mistretta, Leonardo; Rizzo, Raimondo. 2017. "Exploring FPGA‐Based Lock‐In Techniques for Brain Monitoring Applications." Electronics 6, no. 1: 18.