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.