Design and Implementation of a Portable fNIRS Embedded System

Abstract

We report on the design, development and operation of a portable, low cost, battery-operated, multi-channel, functional Near Infrared Spectroscopy embedded system, hosting up to 64 optical sources and 128 Silicon PhotoMultiplier optical detectors. The system is realized as a scalable architecture, whose elementary leaf consists of a probe board provided with 16 SiPMs, 4 couples of bi-color LED, and a temperature sensor, built on a flexible stand. The hardware structure is very versatile because it is possible to handle both the switching time of the LED and the acquisition of the photodetectors, via an ARM based microcontroller.

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

“…This LIA technique employs more hardware resources than the method used in [11], based on a simple moving average filtering of a predefined number of acquired samples, but it generally leads to better results in terms of signal to noise ratio preservation, noise immunity, and low amplitude signal acquisition.…”

“…It is implemented on a flexible PCB (made of Kapton™), capable of being easily adapted, as in typical fNIRS application of brain oxygenation monitoring, to the different shapes of the subject under test. A deeper description of these probe boards can be found in [11].…”

“…The baseline noise does not affect the output as long as it does not saturate the ADC input. The behavior of the system, in this case, is completely different compared to systems that simply acquire ADC values [11] and perform some average filtering. In the latter case, in fact, since the slow baseline variation falls within the Table 1 shows a summary of the main hardware resource utilization within the Xilinx Zynq®-7000 and it is clear that, even if the FIR filter performs with a slightly lower signal distortion than the IIR filter, it requires fourteen times the number of lookup tables and almost thirty times the number of slice registers, hence, exhibiting a much higher power consumption.…”

“…The baseline noise does not affect the output as long as it does not saturate the ADC input. The behavior of the system, in this case, is completely different compared to systems that simply acquire ADC values [11] and perform some average filtering. In the latter case, in fact, since the slow baseline variation falls within the band of the wanted signal, it would thoroughly affect the output, while the LIA system completely eliminates this noise because both of the noise sources lack any component near the lock-in carrier frequency.…”

Exploring FPGA Based Lock-in Techniques for Brain Monitoring Applications

“…Silicon Photomultipliers (SiPM) have been then adopted since they fulfill these fierce requirements [11][12][13][14] and the rest of this paper will describe the system architecture that has been realized and the experimental results that evaluate the obtained performances in term of fidelity on the detecting action and the related computational loads.…”