Method for removing motion artifacts from fNIRS data using ICA and an acceleration sensor

Abstract

Independent component analysis (ICA) is one of the most preferred methods for removing motion artifacts from functional near-infrared spectroscopy (fNIRS) data. In this method, fNIRS signal is separated into some components by ICA. The component which has high correlation between fNIRS signal and motion artifact is determined. This component is removed and fNIRS signal without motion artifact effect is derived. However, because of the influence of blood flow, fNIRS data are often delayed in time compared with the acceleration sensor data. Therefore, the correlation is reduced, and it is difficult to determine whether the component has been derived from the motion artifact. We here propose a method for removing the motion artifact using ICA, which considers the time delay in the fNIRS data. In this proposed method, ICA is performed multiple times, shifting the start time of the fNIRS data with each repeat. Then, only the best correlated result is adopted for comparison with the acceleration sensor data. To examine the effectiveness of this method, its results were compared with the results obtained without considering the time delay. It was found that the proposed method improved that accuracy of removing the motion artifact.

Deep learning-based motion artifact removal in functional near-infrared spectroscopy

“…For example, an acceleration sensor could be used in experiments to capture the motion data. 59 , 60 Short separation detectors were proposed to remove the physiological artifacts 61 – 65 and could also be used to remove motion artifacts. 66 It could also be combined with other filter models to better remove motion artifacts.…”

“…It is also worth mentioning that motion artifact correction is also possible using hardware design changes. For example, an acceleration sensor could be used in experiments to capture the motion data 59 , 60 . Short separation detectors were proposed to remove the physiological artifacts 61 – 65 and could also be used to remove motion artifacts 66 .…”

The NIRS Cap: Key Part of Emerging Wearable Brain-Device Interfaces

“…Most successful fNIRS imaging experiments are commonly conducted inside a lab, where the subject sits still on a chair and is refrained from talking, smiling or moving their head. With the advent of better signal filtration, successful use of fNIRS was also registered in rehabilitation centres with walking patients, or even cycling [5,26,27]; however, constrains on facial expression and subtle head movements still apply, because while certain movement artefacts such walking and running and obvious head movements are easier to isolate and/or filter out, facial expressions are far more difficult to detect. Small facial muscular fluctuations or hair resistance to NIRS optodes that are unnoticeable to outside observers can cause the entire optode holder to slide or cause slight optode inclinations.…”

LIONirs: flexible Matlab toolbox for fNIRS data analysis

“…Independent component analysis (ICA) has largely been used in EEG, and magnetoencephalography (MEG) and has proven to be efficient for removing eye blinks, which generate systematic and reproducible artifacts that can be considered statistically independent from brain activity (Hyvarinen, 1999). The technique of ICA has also been used to unmix the independent components in fMRI and fNIRS (Beckmann and Smith, 2004;Hiroyasu et al, 2013). The use of wavelet decomposition has been proposed for reducing spike artifacts, by removing high-frequency outliers from the data (Molavi and Dumont, 2012).…”