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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, the fNIRS signal is separated into components by ICA and the component that shows high correlation between the fNIRS signal and motion artifact is determined. This component is removed, and the fNIRS signal without motion artifacts is derived. However, fNIRS data are often delayed temporally compared with accelerometer data because the blood flow changes slowly after the subject's head moves. It is necessary to consider the temporal delay in fNIRS data in order to remove motion artifacts when we use ICA method. In this method, the correlation coefficient is used to identify the motion artifact component. However, the cerebral blood flow has a small change because the biological signal fluctuates minutely. Hence, the correlation is reduced, and it is difficult to determine whether the component has been derived from the motion artifact. We propose a method that uses t-tests and the correlation coefficient to identify the motion artifact. In this proposed method, we used t-tests for comparing accelerometer data and signals separated by ICA. The separated signal with no significant difference from accelerometer data were identified as motion artifacts and removed. To examine the validity of this method, we used data sets including motion artifacts caused by sleepiness. Results obtained using only the correlation coefficient were compared with those obtained using the correlation coefficient and t-tests. We found that the proposed method improved that accuracy of removing motion artifacts. In addition, the signs of the accelerometer data were inverted, and t-tests were performed. Consequently, the accuracy of removing the motion artifact was improved.

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The present study investigated the relationship between working memory capacity and brain activity in the dorsolateral prefrontal cortex (DLPFC) and the left ventrolateral prefrontal cortex (VLPFC) using functional near-infrared spectroscopy. We administered the reading span test language task to measure linguistic working memory, while we assessed brain activity reflected in the cerebral blood flow change during the task. The cerebral blood flow change in the DLPFC had a negative correlation with the reading span test score. Both focused regions exhibited different patterns of cerebral blood flow changes during the first and second halves of the task period in the high-and low-score groups. Our findings suggest that the brain activity correlates with the reading span test score, which is associated with linguistic working memory capacity. Therefore, brain activity measurement in the DLPFC with the functional near-infrared spectroscopy can be used to indirectly assess working memory capacity.