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Detecting Motor Learning-Related fNIRS Activity by Applying Removal of Systemic Interferences
Isao NAMBU Takahiro IMAI Shota SAITO Takanori SATO Yasuhiro WADA
IEICE TRANSACTIONS on Information and Systems
Publication Date: 2017/01/01
Online ISSN: 1745-1361
Type of Manuscript: LETTER
Category: Biological Engineering
fNIRS, brain, motor learning, scalp artifact reduction,
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Functional near-infrared spectroscopy (fNIRS) is a noninvasive neuroimaging technique, suitable for measurement during motor learning. However, effects of contamination by systemic artifacts derived from the scalp layer on learning-related fNIRS signals remain unclear. Here we used fNIRS to measure activity of sensorimotor regions while participants performed a visuomotor task. The comparison of results using a general linear model with and without systemic artifact removal shows that systemic artifact removal can improve detection of learning-related activity in sensorimotor regions, suggesting the importance of removal of systemic artifacts on learning-related cerebral activity.