scholarly journals Methodology for high-yield acquisition of functional near-infrared spectroscopy data from alert, upright infants

2016 ◽  
Vol 3 (3) ◽  
pp. 031415
Author(s):  
James R. Goodwin ◽  
Ashley E. Cannaday ◽  
Holly G. Palmeri ◽  
Aldo Di Costanzo ◽  
Lauren L. Emberson ◽  
...  
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
High Yield ◽  
Spectroscopy Data ◽  
Functional Near Infrared Spectroscopy

scholarly journals Comparison of motion correction techniques applied to functional near-infrared spectroscopy data from children

2015 ◽  
Vol 20 (12) ◽  
pp. 126003 ◽  
Author(s):  
Xiao-Su Hu ◽  
Maria M. Arredondo ◽  
Megan Gomba ◽  
Nicole Confer ◽  
Alexandre F. DaSilva ◽  
...  
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Motion Correction ◽  
Near Infrared ◽  
Spectroscopy Data ◽  
Functional Near Infrared Spectroscopy

scholarly journals Comparison of motion correction techniques applied to functional near-infrared spectroscopy data from children (Erratum)

2020 ◽  
Vol 25 (05) ◽  
pp. 1
Author(s):  
Xiao-Su Hu ◽  
Maria M. Arredondo ◽  
Megan Gomba ◽  
Nicole Confer ◽  
Alexandre F. DaSilva ◽  
...  
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Motion Correction ◽  
Near Infrared ◽  
Spectroscopy Data ◽  
Functional Near Infrared Spectroscopy

scholarly journals No Difference in the Neural Underpinnings of Number and Letter Copying in Children: Bayesian Analysis of Functional Near‐Infrared Spectroscopy Data

2019 ◽  
Vol 13 (4) ◽  
pp. 313-325
Author(s):  
Mojtaba Soltanlou ◽  
Andra Coldea ◽  
Christina Artemenko ◽  
Ann‐Christine Ehlis ◽  
Andreas J. Fallgatter ◽  
...  
Keyword(s):  
Infrared Spectroscopy ◽  
Bayesian Analysis ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Spectroscopy Data ◽  
Functional Near Infrared Spectroscopy ◽  
Neural Underpinnings

scholarly journals FC-NIRS: A Functional Connectivity Analysis Tool for Near-Infrared Spectroscopy Data

2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
Author(s):  
Jingping Xu ◽  
Xiangyu Liu ◽  
Jinrui Zhang ◽  
Zhen Li ◽  
Xindi Wang ◽  
...  
Keyword(s):  
Infrared Spectroscopy ◽  
Functional Connectivity ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Analysis Tool ◽  
Spectroscopy Data ◽  
Connectivity Analysis ◽  
Functional Near Infrared Spectroscopy ◽  
Functional Connectome ◽  
Functional Connectivity Analysis

Functional near-infrared spectroscopy (fNIRS), a promising noninvasive imaging technique, has recently become an increasingly popular tool in resting-state brain functional connectivity (FC) studies. However, the corresponding software packages for FC analysis are still lacking. To facilitate fNIRS-based human functional connectome studies, we developed a MATLAB software package called “functional connectivity analysis tool for near-infrared spectroscopy data” (FC-NIRS). This package includes the main functions of fNIRS data preprocessing, quality control, FC calculation, and network analysis. Because this software has a friendly graphical user interface (GUI), FC-NIRS allows researchers to perform data analysis in an easy, flexible, and quick way. Furthermore, FC-NIRS can accomplish batch processing during data processing and analysis, thereby greatly reducing the time cost of addressing a large number of datasets. Extensive experimental results using real human brain imaging confirm the viability of the toolbox. This novel toolbox is expected to substantially facilitate fNIRS-data-based human functional connectome studies.

ICNA: A software tool for manifold embedded based analysis of functional near infrared spectroscopy data

NeuroImage ◽  
2009 ◽  
Vol 47 ◽  
pp. S59 ◽  
Author(s):  
F. Orihuela-Espina ◽  
D.R. Leff ◽  
D.A. James ◽  
A.W. Darzi ◽  
G-Z. Yang
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Software Tool ◽  
Spectroscopy Data ◽  
Functional Near Infrared Spectroscopy

Classification of affect using deep learning on brain blood flow data

2019 ◽  
Vol 27 (3) ◽  
pp. 206-219 ◽  
Author(s):  
Danushka Bandara ◽  
Leanne Hirshfield ◽  
Senem Velipasalar
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Classification Accuracy ◽  
Near Infrared ◽  
Time Series Data ◽  
Self Report ◽  
Series Data ◽  
Spectroscopy Data ◽  
Short Term ◽  
Functional Near Infrared Spectroscopy

We present a convolutional neural network- and long short-term memory-based method to classify the valence level of a computer user based on functional near infrared spectroscopy data. Convolutional neural networks are well suited for capturing the spatial characteristics of functional near infrared spectroscopy data. And long short-term memories are demonstrated to be good at learning temporal patterns of unknown length in time series data. We explore these methods in a combined layered architecture in order to improve classification accuracy. We conducted an experiment with 20 participants, wherein they were subjected to emotion inducing stimuli while their brain activity was measured using functional near infrared spectroscopy. Self-report surveys were administered after each stimulus to gauge participants' self-assessment of their valence. The resulting classification using these survey labels as ground truth provided a three-class classification accuracy 77.89% in across subject cross-validation. This method also shows promise for generalization to other classification tasks using functional near infrared spectroscopy data.

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