Detecting Motor Learning-Related fNIRS Activity by Applying Removal of Systemic Interferences
Summary :
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.
- Publication
- IEICE TRANSACTIONS on Information Vol.E100-D No.1 pp.242-245
- Publication Date
- 2017/01/01
- Publicized
- 2016/10/04
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.2016EDL8132
- Type of Manuscript
- LETTER
- Category
- Biological Engineering
Authors
Isao NAMBU
Nagaoka University of Technology
Takahiro IMAI
Nagaoka University of Technology
Shota SAITO
Nagaoka University of Technology
Takanori SATO
Nagaoka University of Technology
Yasuhiro WADA
Nagaoka University of Technology
Keyword
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Isao NAMBU, Takahiro IMAI, Shota SAITO, Takanori SATO, Yasuhiro WADA, "Detecting Motor Learning-Related fNIRS Activity by Applying Removal of Systemic Interferences" in IEICE TRANSACTIONS on Information,
vol. E100-D, no. 1, pp. 242-245, January 2017, doi: 10.1587/transinf.2016EDL8132.
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.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2016EDL8132/_p
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@ARTICLE{e100-d_1_242,
author={Isao NAMBU, Takahiro IMAI, Shota SAITO, Takanori SATO, Yasuhiro WADA, },
journal={IEICE TRANSACTIONS on Information},
title={Detecting Motor Learning-Related fNIRS Activity by Applying Removal of Systemic Interferences},
year={2017},
volume={E100-D},
number={1},
pages={242-245},
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.},
keywords={},
doi={10.1587/transinf.2016EDL8132},
ISSN={1745-1361},
month={January},}
Copy
TY - JOUR
TI - Detecting Motor Learning-Related fNIRS Activity by Applying Removal of Systemic Interferences
T2 - IEICE TRANSACTIONS on Information
SP - 242
EP - 245
AU - Isao NAMBU
AU - Takahiro IMAI
AU - Shota SAITO
AU - Takanori SATO
AU - Yasuhiro WADA
PY - 2017
DO - 10.1587/transinf.2016EDL8132
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E100-D
IS - 1
JA - IEICE TRANSACTIONS on Information
Y1 - January 2017
AB - 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.
ER -
English
