Removal of the skin blood flow artifact in functional near-infrared spectroscopic imaging data through independent component analysis

2007 ◽  
Vol 12 (6) ◽  
pp. 062111 ◽  
Author(s):  
Satoru Kohno ◽  
Ichiro Miyai ◽  
Akitoshi Seiyama ◽  
Ichiro Oda ◽  
Akihiro Ishikawa ◽  
...  
Keyword(s):  
Blood Flow ◽  
Independent Component Analysis ◽  
Skin Blood Flow ◽  
Near Infrared ◽  
Spectroscopic Imaging ◽  
Component Analysis ◽  
Independent Component ◽  
Imaging Data ◽  
Flow Artifact ◽  
Infrared Spectroscopic Imaging

scholarly journals 3I1500 Near infrared spectroscopic imaging can be affected by skin blood flow

2002 ◽  
Vol 42 (supplement2) ◽  
pp. S176
Author(s):  
H. Eda ◽  
A. Takatsuki ◽  
I. Sase ◽  
A. Seiyama ◽  
S. Miyauchi ◽  
...  
Keyword(s):  
Blood Flow ◽  
Skin Blood Flow ◽  
Near Infrared ◽  
Spectroscopic Imaging ◽  
Infrared Spectroscopic ◽  
Infrared Spectroscopic Imaging

scholarly journals NIRS-ICA: A MATLAB Toolbox for Independent Component Analysis Applied in fNIRS Studies

2021 ◽  
Vol 15 ◽  
Author(s):  
Yang Zhao ◽  
Pei-Pei Sun ◽  
Fu-Lun Tan ◽  
Xin Hou ◽  
Chao-Zhe Zhu
Keyword(s):  
Independent Component Analysis ◽  
Neuronal Activity ◽  
Near Infrared ◽  
Component Analysis ◽  
Independent Component ◽  
Imaging Data ◽  
Matlab Toolbox ◽  
Functional Near Infrared Spectroscopy ◽  
Source Selection ◽  
Brain Imaging Data

Independent component analysis (ICA) is a multivariate approach that has been widely used in analyzing brain imaging data. In the field of functional near-infrared spectroscopy (fNIRS), its promising effectiveness has been shown in both removing noise and extracting neuronal activity-related sources. The application of ICA remains challenging due to its complexity in usage, and an easy-to-use toolbox dedicated to ICA processing is still lacking in the fNIRS community. In this study, we propose NIRS-ICA, an open-source MATLAB toolbox to ease the difficulty of ICA application for fNIRS studies. NIRS-ICA incorporates commonly used ICA algorithms for source separation, user-friendly GUI, and quantitative evaluation metrics assisting source selection, which facilitate both removing noise and extracting neuronal activity-related sources. The options used in the processing can also be reported easily, which promotes using ICA in a more reproducible way. The proposed toolbox is validated and demonstrated based on both simulative and real fNIRS datasets. We expect the release of the toolbox will extent the application for ICA in the fNIRS community.

Independent component analysis to proton spectroscopic imaging data of human brain tumours

2005 ◽  
Vol 56 (2) ◽  
pp. 160-164 ◽  
Author(s):  
J. Pulkkinen ◽  
A.-M. Häkkinen ◽  
N. Lundbom ◽  
A. Paetau ◽  
R.A. Kauppinen ◽  
...  
Keyword(s):  
Independent Component Analysis ◽  
Human Brain ◽  
Brain Tumours ◽  
Spectroscopic Imaging ◽  
Component Analysis ◽  
Independent Component ◽  
Imaging Data ◽  
Human Brain Tumours

Application of independent component analysis to 1H MR spectroscopic imaging exams of brain tumours

2005 ◽  
Vol 544 (1-2) ◽  
pp. 36-46 ◽  
Author(s):  
F. Szabo de Edelenyi ◽  
A.W. Simonetti ◽  
G. Postma ◽  
R. Huo ◽  
L.M.C. Buydens
Keyword(s):  
Independent Component Analysis ◽  
Brain Tumours ◽  
Spectroscopic Imaging ◽  
Component Analysis ◽  
Independent Component ◽  
Mr Spectroscopic Imaging
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