Measuring Cerebral Activation From fNIRS Signals: An Approach Based on Compressive Sensing and Taylor-Fourier Model

• Published in: IEEE transactions on instrumentation and measurement Vol. 65; no. 6; pp. 1310 - 1318
• Main Authors: Frigo, Guglielmo, Brigadoi, Sabrina, Giorgi, Giada, Sparacino, Giovanni, Narduzzi, Claudio
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.06.2016; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Biomedical monitoring; Compressive sensing (CS); denoising; Discrete Fourier transforms; functional near-infrared spectroscopy (fNIRS); haemodynamic response (HR); Heart rate; Noise measurement; Noise reduction; physiological noise; Physiology; short-separation (SS) channels; Taylor-Fourier modeling; denoising; short-separation (SS) channels; Compressive sensing (CS); functional near-infrared spectroscopy (fNIRS); physiological noise; haemodynamic response (HR); Taylor–Fourier modeling
• ISSN: 0018-9456; 1557-9662; 1557-9662
• DOI: 10.1109/TIM.2016.2518363

Functional near-infrared spectroscopy (fNIRS) is a noninvasive and portable neuroimaging technique that uses NIR light to monitor cerebral activity by the so-called haemodynamic responses (HRs). The measurement is challenging because of the presence of severe physiological noise, such as respiratory and vasomotor waves. In this paper, a novel technique for fNIRS signal denoising and HR estimation is described. The method relies on a joint application of compressed sensing theory principles and Taylor-Fourier modeling of nonstationary spectral components. It operates in the frequency domain and models physiological noise as a linear combination of sinusoidal tones, characterized in terms of frequency, amplitude, and initial phase. Algorithm performance is assessed over both synthetic and experimental data sets, and compared with that of two reference techniques from fNIRS literature.