Paper Info
Title
Classification Analysis of Chronological Age Using Brief Resting Electroencephalographic (EEG) Recordings
Abstract
The present study aims to build a classification model that discriminates between chronological ages of subjects based on resting-state electroencephalography (EEG) data collected from a community sample of 269 children aged 7 to 11. Specifically, spectral power densities in four classical frequency bands: Delta (0.5-3 Hz), Theta (4-7 Hz), Alpha (8-12 Hz) and Beta (14-25 Hz) were extracted for each electrode as features, and fed to three classification algorithms including logistic regression (LR), support vector machine (SVM), and least absolute shrinkage and selection operator (Lasso). In addition, principal component analysis (PCA) was used to reduce the dimensions of the feature space. The results demonstrated that SVM and Lasso evidenced better performance (maximal accuracy = 80.68 +/- 2.01% by SVM and 77.82 +/- 2.11% by Lasso) when applied to original feature space, but LR yielded the best performance with PCA (80.72 +/- 1.73%). The accuracy of binary classification exhibited a decreasing trend with diminishing chronological gaps between the groups.
Year
DOI
Venue
2015
10.1007/978-3-319-23344-4_10
Lecture Notes in Artificial Intelligence
Keywords
Field
DocType
Electroencephalography,Brain development,Adolescence,Machine learning,Pattern classification
Feature vector,Pattern recognition,Binary classification,Support vector machine,Lasso (statistics),Artificial intelligence,Statistical classification,Logistic regression,Principal component analysis,Electroencephalography,Mathematics
Conference
Volume
ISSN
Citations 
9250
0302-9743
0
PageRank 
References 
Authors
0.34
1
6
Name
Order
Citations
PageRank
Miaolin Fan141.09
Vladimir Miskovic222.08
Chun-An Chou36813.88
Sina Khanmohammadi4444.92
Hiroki Sayama531949.14
Brandon E. Gibb620.73