A Performance Indicator for Optimizing Source–Detector Separation in Functional Near-Infrared Spectroscopy

Abstract

The performance of Functional Near-Infrared Spectroscopy (fNIRS) devices critically depends on the probe design, which affects signal quality, spatial and depth resolution, and data reliability. A critical component of probe separation is source-to-detector separation, which is defined as the distance between the light source and the detector. Optimizing this separation is essential for improving the signal-to-noise ratio (SNR) and sensitivity at depth (SAD). Larger separations enhance depth resolution, facilitating more accurate assessments of brain activity. Conversely, excessive separation may reduce SNR due to the lower light intensity received by the detector. In this study, a performance indicator was created to optimize separation by integrating the SNR and SAD. A probe was constructed that featured one light source and four detectors mounted on a mechanism that allowed for adjustable separations. A phantom mimicking brain tissue was used. Signals were recorded from the probe positioned on the phantom at various separations, employing light sources emitting light at wavelengths of 730, 800, and 850 nm, and optical power levels of 19, 26, 32, 38, and 44 mW. The SNR values for each separation were computed from the recorded signals, whereas the SAD values were obtained from existing literature. The performance indicator was developed as a weighted sum of SNR and SAD, normalized between 0 and 1, with higher values indicating enhanced probe performance due to optimized separation. The indicator is expected to improve the reliability of fNIRS data; however, further research involving diverse populations is required to validate its practical application.