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

Yan Zhang,Xin Liu,Jinwei Sun,Dan Liu,Qisong Wang,Chunling Yang,Kuanquan Wang

doi:10.1155/2018/7609713

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.

Highlights

Functional near-infrared spectroscopy has been demonstrated to have the potential to discover hemodynamic variations within the cortex [1, 2]
Low-pass filtering (LPF) or band-pass filtering (BPF) technique is a simple method to remove the interference caused by cardiac oscillations
Based on the single channel probe arrangement, empirical mode decomposition (EMD) algorithm together with Hilbert transform was presented to be an effective method for suppressing the physiological interferences from the Functional near-infrared spectroscopy (fNIRS) signal

Summary

Introduction

Functional near-infrared spectroscopy (fNIRS) has been demonstrated to have the potential to discover hemodynamic variations within the cortex [1, 2]. The useful signal is in the deeper regions of the brain and the strong mixture between the physiological interference and the brain activity response presents significant challenges in signal extraction [5] The suppression of this kind of physiological interference is very important to accurately access the brain function in fNIRS measurement. Based on the single channel probe arrangement, empirical mode decomposition (EMD) algorithm together with Hilbert transform was presented to be an effective method for suppressing the physiological interferences from the fNIRS signal. This methodology has the advantage of simplicity in instrument design and the possibility for the application in optical topography [7]

Methods

Results

Discussion

Conclusion