The present study aims to build a classification model that discrimi- nates between chronological ages of subjects based on resting-state electroence- phalography (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 ex- tracted for each electrode as features, and fed to three classification algorithms including logistic regression (LR), support vector machine (SVM), and least abso- lute shrinkage and selection operator (Lasso). In addition, principal component analysis (PCA) was used to reduce the dimensions of the feature space. The re- sults 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.
['Miaolin Fan', 'Vladimir Miskovic', 'Chun-An Chou', 'Sina Khanmohammadi', 'Hiroki Sayama', 'Brandon E. Gibb']
Classification Analysis of Chronological Age Using Brief Resting Electroencephalographic (EEG) Recordings