Evaluating Motion Processing Algorithms for Use with Functional Near-infrared Spectroscopy Data from Young Children

Abstract

Motion artifacts are often a significant component of the measured signal in functional near infrared spectroscopy (fNIRS) experiments. A variety of methods have been proposed to address this issue, including principal component analyses (PCA), correlation-based signal improvement (CBSI), wavelet filtering, and spline interpolation. The efficacy of these techniques has been compared using simulated data; however, our understanding of how these techniques fare when dealing with task-based cognitive data is limited. Brigadoi et al. (2014) compared motion correction techniques in a sample of adult data measured during a simple cognitive task. Wavelet filtering showed the most promise as an optimal technique for motion correction. Given that fNIRS is often used with infants and young children, it is critical to evaluate the effectiveness of motion correction techniques directly with data from these age groups. This study addresses that problem by evaluating motion correction algorithms implemented in HomER2. The efficacy of each technique was compared quantitatively using objective metrics related to the physiological properties of the hemodynamic response. Results showed that targeted PCA (tPCA), Spline, and CBSI retained a higher number of trials. These techniques also performed well in direct head-to head comparisons with the other approaches using quantitative metrics. The CBSI method corrected many of the artifacts present in our data; however, this approach produced sometimes unstable HRFs. The targeted PCA and Spline methods proved to be the most robust, performing well across all comparison metrics. When compared head-to-head, tPCA consistently outperformed Spline. We conclude, therefore, that tPCA is an effective technique for correcting motion artifacts in fNIRS data from young children.