Back

NIRDuino: A modular, Bluetooth-enabled, Android-configurable fNIRS system with dual-intensity mode built on Arduino

Kumar, A.; Crawford, S.; Le, T.-C.; Rahimpour Jounghani, A.; Moreno Carbonell, L.; Sargent Capps, A. A.; Walter, A.; Liu, D.; Sullivan, R.; Jansen, E. D.; Hosseini, S. H.; Bowden, A. K. E.

2024-12-24 psychiatry and clinical psychology
10.1101/2024.12.20.24318425 medRxiv
Show abstract

SignificanceWe present NIRDuino: an Open-source Android(R)-configurable, modular, and Bluetooth-enabled fNIRS system that allows researchers to perform neuroimaging studies with up to eight emitters and 16 detectors. The complete system (including Android tablet) can be assembled for less than $1000, and the emitters and detectors can be arranged in any configuration to achieve the desired short and long channels required for their study. AimThe system has been designed with non-engineers in mind, and the researcher only needs to design the wearable interfaces to attach the emitters and detectors to the body appropriate for their intended application. ApproachThe system consists of a battery-powered, wireless controller built on the Arduino(R) Nano ESP32 platform, a dongle with sockets for each of the eight emitters and detectors that can be connected, and individual wired probes for emitters and detectors. In accompaniment, Arduino(R)-based firmware and an Android(R) application have also been developed and provided. The selected emitters and detectors can be arranged in any desired configuration, and the emitters can be configured to output light with both regular intensities and low intensities to collect data for "long channels" with sufficient signal quality and "short channels" without saturation. This paper details the systems design and characterization on phantom and two physiological experiences on a human. ResultsThe easy-to-configure hardware/software system demonstrated stability in fNIRS measurements using a single emitter-detector pair placed on a phantom, and reproduced previously published outcomes for arterial cuff measurements on the forearm and a arithmetic experiment on the forehead. ConclusionThe NIRDuino circuitry and software demonstrated modularity and usability for NIRS experiments, and this low-cost platform will provide researchers globally with an affordable fNIRS system to easily adopt and adapt for their unique experimental needs.

Matching journals

The top 6 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.