1. Adaptive filtering of physiological noises in fNIRS data
- Author
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Hoang-Dung Nguyen, So-Hyeon Yoo, M. Raheel Bhutta, and Keum-Shik Hong
- Subjects
Functional near-infrared spectroscopy (fNIRS) ,Hemodynamic response (HR) ,Recursive least squares estimation (RLSE) ,Exponential forgetting ,Real time estimation ,State space model ,Medical technology ,R855-855.5 - Abstract
Abstract The study presents a recursive least-squares estimation method with an exponential forgetting factor for noise removal in functional near-infrared spectroscopy data and extraction of hemodynamic responses (HRs) from the measured data. The HR is modeled as a linear regression form in which the expected HR, the first and second derivatives of the expected HR, a short-separation measurement data, three physiological noises, and the baseline drift are included as components in the regression vector. The proposed method is applied to left-motor-cortex experiments on the right thumb and little finger movements in five healthy male participants. The algorithm is evaluated with respect to its performance improvement in terms of contrast-to-noise ratio in comparison with Kalman filter, low-pass filtering, and independent component method. The experimental results show that the proposed model achieves reductions of 77% and 99% in terms of the number of channels exhibiting higher contrast-to-noise ratios in oxy-hemoglobin and deoxy-hemoglobin, respectively. The approach is robust in obtaining consistent HR data. The proposed method is applied for both offline and online noise removal.
- Published
- 2018
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