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IEICE Transactions on Information and Systems
Online ISSN : 1745-1361
Print ISSN : 0916-8532
Regular Section
Detecting Motor Learning-Related fNIRS Activity by Applying Removal of Systemic Interferences
Isao NAMBUTakahiro IMAIShota SAITOTakanori SATOYasuhiro WADA
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JOURNAL FREE ACCESS

2017 Volume E100.D Issue 1 Pages 242-245

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Abstract

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.

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© 2017 The Institute of Electronics, Information and Communication Engineers
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