Evoked Hemodynamic Response Estimation to Auditory Stimulus Using Recursive Least Squares Adaptive Filtering with Multidistance Measurement of Near-Infrared Spectroscopy

Abstract

The performance of functional near-infrared spectroscopy (fNIRS) is sometimes degraded by the interference caused by the physical or the systemic physiological activities. Several interferences presented during fNIRS recordings are mainly induced by cardiac pulse, breathing, and spontaneous physiological low-frequency oscillations. In previous work, we introduced a multidistance measurement to reduce physiological interference based on recursive least squares (RLS) adaptive filtering. Monte Carlo simulations have been implemented to evaluate the performance of RLS adaptive filtering. However, its suitability and performance on human data still remain to be evaluated. Here, we address the issue of how to detect evoked hemodynamic response to auditory stimulus using RLS adaptive filtering method. A multidistance probe based on continuous wave fNIRS is devised to achieve the fNIRS measurement and further study the brain functional activation. This study verifies our previous findings that RLS adaptive filtering is an effective method to suppress global interference and also provides a practical way for real-time detecting brain activity based on multidistance measurement.

Evaluation of fNIRS Signal Components Elicited by Cognitive and Hypercapnic Stimuli

“…Furthermore, VLF band power from long-SDS measurements were greater than that of short-SDS measurements (see Post-hoc SDS columns of Table 1) in cognitive task but did not differ in hypercapnic task (see Post-hoc SDS columns of Table 2). Studies have indicated that such activity is likely due to scalp blood flow (SBF) [34][35][36][37][38][39][40][41] . Based on this, the results in this study indicate that measurements taken from long SDS channels reflect a combination of hemodynamic changes associated with NVC and SBF in cognitive task and strictly SBF in hypercapnic task.…”

“…Alternatively, methods utilizing the addition of short source-detector separation (SDS) channels (0.5-1.0 cm separation) to the fNIRS sensor alongside the long SDS channels (2-4 cm separation) allow for direct separation of signals that have penetrated the cerebral layer and those that have not. The most direct techniques that utilize short SDS channels are the least squares adaptive filters and regression modelling [37][38][39][40][41] . Although these techniques improve contrast-tonoise ratio by 70%, they assume that extracerebral contributions are static and spatially homogenous, which is not always accurate 28,35,42 .…”

Toward Quantitative Near Infrared Brain Functional Imaging: Lock-In Photon Counting Instrumentation Combined With Tomographic Reconstruction

“…In the above experiments, the semi-3D DOT algorithm that uses only the 2nd NN channel data is developed to significantly reduce the unknowns by incorporating the priori information on the activation layer position as well as assumption that the absorption perturbation is invariant along the depth direction. Nevertheless, many studies suggest that the taskrelated hemodynamics and physiological noises may induce the absorption perturbation in the scalp in addition to the cerebral cortex (gray matter) [35], [36]. In view of this fact, it is necessary to extend the proposed semi-3D algorithm for the double layer reconstruction or develop a full-3D method that enables depth resolution [32], [37].…”