Validity of decision mode analysis on an ROI determination problem in multichannel fNIRS data

Validity of decision mode analysis on an ROI determination problem in multichannel fNIRS data Region of interest (ROI) determination is necessary when using functional near-infrared spectroscopy (fNIRS) data to detect brain activity. To extract ROIs from multiple fNIRS channels, we investigated the validity of applying decision mode analysis to the fNIRS dataset. This classifies a dataset into clusters with similar features. For each cluster, the dataset is decomposed into a mean vector and a linear combination of eigenvectors. Applying this to fNIRS signals, the mean vector can be used to represent change in hemoglobin (Hb), and the eigenvectors interpreted as a signal component constructing the arbitrary signal. Characterizing these vectors by correlating them with a theoretical model of brain function aids our understanding of where Hb changes occur and what type of Hb changes reflect brain activity in fNIRS data. Decision mode analysis of fNIRS data measured during viewing stereoscopic images identified ROIs around the right inferior frontal gyrus associated with attentional control, and frontal association area associated with decision on action and prediction. Our experimental results showed that information obtained from decision mode analysis can aid quantitative and qualitative ROI determination. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Artificial Life and Robotics Springer Journals

Validity of decision mode analysis on an ROI determination problem in multichannel fNIRS data

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Publisher
Springer Japan
Copyright
Copyright © 2017 by ISAROB
Subject
Computer Science; Artificial Intelligence (incl. Robotics); Computation by Abstract Devices; Control, Robotics, Mechatronics
ISSN
1433-5298
eISSN
1614-7456
D.O.I.
10.1007/s10015-017-0362-5
Publisher site
See Article on Publisher Site

Abstract

Region of interest (ROI) determination is necessary when using functional near-infrared spectroscopy (fNIRS) data to detect brain activity. To extract ROIs from multiple fNIRS channels, we investigated the validity of applying decision mode analysis to the fNIRS dataset. This classifies a dataset into clusters with similar features. For each cluster, the dataset is decomposed into a mean vector and a linear combination of eigenvectors. Applying this to fNIRS signals, the mean vector can be used to represent change in hemoglobin (Hb), and the eigenvectors interpreted as a signal component constructing the arbitrary signal. Characterizing these vectors by correlating them with a theoretical model of brain function aids our understanding of where Hb changes occur and what type of Hb changes reflect brain activity in fNIRS data. Decision mode analysis of fNIRS data measured during viewing stereoscopic images identified ROIs around the right inferior frontal gyrus associated with attentional control, and frontal association area associated with decision on action and prediction. Our experimental results showed that information obtained from decision mode analysis can aid quantitative and qualitative ROI determination.

Journal

Artificial Life and RoboticsSpringer Journals

Published: Apr 1, 2017

References

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