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Graph Frequency Analysis of Brain Signals

Abstract: 

This paper presents methods to analyze functional brain networks and signals from graph spectral perspectives. The notion of frequency and filters traditionally defined for signals supported on regular domains such as discrete time and image grids has been recently generalized to irregular graph domains, and defines brain graph frequencies associated with different levels of spatial smoothness across the brain regions. Brain network frequency also enables the decomposition of brain signals into pieces corresponding to smooth or rapid variations. We relate graph frequency with principal component analysis when the networks of interest denote functional connectivity. The methods are utilized to analyze brain networks and signals as subjects master a simple motor skill. We observe that brain signals corresponding to different graph frequencies exhibit different levels of adaptability throughout learning. Further, we notice a strong association between graph spectral properties of brain networks and the level of exposure to tasks performed, and recognize the most contributing and important frequency signatures at different levels of task familiarity.

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Paper Details

Authors:
Weiyu Huang, Leah Goldsberry, Nicholas F. Wymbs, Scott T. Grafton, Danielle S. Bassett, and Alejandro Ribeiro
Submitted On:
19 July 2016 - 1:47pm
Short Link:
Type:
Research Manuscript
Event:
Presenter's Name:
Leah Goldsberry
Paper Code:
spm1227
Document Year:
2016
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Document Files

projectSummary_2016_IEEE.pdf

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[1] Weiyu Huang, Leah Goldsberry, Nicholas F. Wymbs, Scott T. Grafton, Danielle S. Bassett, and Alejandro Ribeiro, "Graph Frequency Analysis of Brain Signals", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/1134. Accessed: Apr. 24, 2017.
@article{1134-16,
url = {http://sigport.org/1134},
author = {Weiyu Huang; Leah Goldsberry; Nicholas F. Wymbs; Scott T. Grafton; Danielle S. Bassett; and Alejandro Ribeiro },
publisher = {IEEE SigPort},
title = {Graph Frequency Analysis of Brain Signals},
year = {2016} }
TY - EJOUR
T1 - Graph Frequency Analysis of Brain Signals
AU - Weiyu Huang; Leah Goldsberry; Nicholas F. Wymbs; Scott T. Grafton; Danielle S. Bassett; and Alejandro Ribeiro
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/1134
ER -
Weiyu Huang, Leah Goldsberry, Nicholas F. Wymbs, Scott T. Grafton, Danielle S. Bassett, and Alejandro Ribeiro. (2016). Graph Frequency Analysis of Brain Signals. IEEE SigPort. http://sigport.org/1134
Weiyu Huang, Leah Goldsberry, Nicholas F. Wymbs, Scott T. Grafton, Danielle S. Bassett, and Alejandro Ribeiro, 2016. Graph Frequency Analysis of Brain Signals. Available at: http://sigport.org/1134.
Weiyu Huang, Leah Goldsberry, Nicholas F. Wymbs, Scott T. Grafton, Danielle S. Bassett, and Alejandro Ribeiro. (2016). "Graph Frequency Analysis of Brain Signals." Web.
1. Weiyu Huang, Leah Goldsberry, Nicholas F. Wymbs, Scott T. Grafton, Danielle S. Bassett, and Alejandro Ribeiro. Graph Frequency Analysis of Brain Signals [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/1134