Removal of motion artifacts originating from optode fluctuations during functional near-infrared spectroscopy measurements

Abstract

Functional near-infrared spectroscopy (fNIRS) has been increasingly utilized for detecting human cerebral activity in many disciplines because of the potential for less-restraining conditions. However, users often suffer from motion artifacts originating from optode fluctuation during task execution when the task includes motion. In such cases, the optode fluctuation induces changes both in the reflection by hair and in the transmission between the optode and scalp. If part of the reflected light is directly received by the detector optode (short-circuited light), it will contaminate the fNIRS signal. The transmittance change at the optode-scalp gap will also contaminate the signal. In this study, we proposed an optical model on the influence of optode fluctuation on the fNIRS signal and a method for removing the influence. The model revealed the following: (1) the received short-circuited light and the gap transmittance change generated a baseline change in the detected light intensity, and (2) the signal from the tissues was downscaled with increases in the receiving intensity of short-circuited light. To avoid erroneous detection of short-circuited light, we developed a method that optically eliminated hair-reflected light from the detection using linearly polarized light sources and an orthogonally polarized analyzer. The method was validated with an optical phantom possessing a haired surface. The optical absorbance change of a close source-detector (S-D) pair equipped with polarizers was very similar to that of distant S-D pairs, even though these optodes were artificially fluctuated. By combining the multidistance optode arrangement technique with the short-circuited light elimination method, the measurement could effectively eliminate motion artifacts originating from optode fluctuation.

Partial correlation-based functional connectivity analysis for functional near-infrared spectroscopy signals

“…Several researchers have proposed to use a second detector placed closer to the source (short separation detector) that will be sensitive to changes only on the superficial layers to regress out the systemic fluctuations contaminating a detector placed farther away from the source. 2,6,[9][10][11][12][13][14][15] The use of an SSD has been investigated deeply due to its ambiguity of its placement (how short is short enough?). 11 It also increases instrumentation and data analysis complexity, not to mention the extra burden it brings to ergonomics of the probe.…”

Reliability of Frontal Eye Fields Activation and Very Low-Frequency Oscillations Observed during Vergence Eye Movements: an fNIRS Study

“…FNIRS capitalizes on the different absorption characteristics of oxygenated (HbO) and deoxygenated (HbR) hemoglobin when exposed to 600-900 nm infrared light to detect individual differences of HbO and HbR concentrations 1,2 . The scattered and re-emitted infrared light is measured by a detector placed between 10-40 mm from a source [3][4][5][6] . This source-detector distance induces a light penetration depth of 2-8 mm into the cortex 7 .…”

“…For the scattering and recapture of infrared light, optimal optode placement requires that the optodes have direct contact with a participant's scalp. Anything less than an orthogonal contact by the optode with the scalp will result in extra-cranial scattering of light, creating noise artifacts within the collected data 5 . A high signal-to-noise ratio (SNR) is attained by housing the optodes in a holder called a head cap.…”

Evaluating motion processing algorithms for use with functional near-infrared spectroscopy data from young children

“…Among these approaches are principal component analysis (PCA), 19 Kalman filtering, 20 correlation-based signal improvement (CBSI), 21 wavelet filtering, 22 spline interpolation, 23 autoregressive algorithms, 24 and more recently, a kurtosis-based wavelet algorithm, 25 empirical mode decomposition (EMD), 26 and an optical model on the influence of optode fluctuation on the fNIRS signal. 27 Several papers have explored the efficacy of different motion correction techniques for fNIRS data. 12,17,[25][26][27][28][29][30][31][32] The majority of these reports have investigated this problem by adding a simulated hemodynamic response to resting-state data.…”

“…27 Several papers have explored the efficacy of different motion correction techniques for fNIRS data. 12,17,[25][26][27][28][29][30][31][32] The majority of these reports have investigated this problem by adding a simulated hemodynamic response to resting-state data. Recently, Chiarelli et al 25 introduced a kurtosis-based wavelet algorithm that proved to be more efficient in removing motion artifacts when compared with other techniques in a resting-state dataset.…”