Classification analysis of chronological age using brief resting electroencephalographic (EEG) recordings

Miaolin Fan*, Vladimir Miskovic, Chun An Chou, Sina Khanmohammadi, Hiroki Sayama, Brandon E. Gibb

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution


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.

Original languageEnglish
Title of host publicationBrain Informatics and Health - 8th International Conference, BIH 2015, Proceedings
EditorsYike Guo Y., Sean Hill S., Karl Friston, Hanchuan Peng, Aldo Faisal A.
PublisherSpringer Verlag
Number of pages9
ISBN (Print)9783319233437
Publication statusPublished - 2015
Externally publishedYes
Event8th International Conference on Brain Informatics and Health, BIH 2015 - London, United Kingdom
Duration: 2015 Aug 302015 Sept 2

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Other8th International Conference on Brain Informatics and Health, BIH 2015
Country/TerritoryUnited Kingdom


  • Adolescence
  • Brain development
  • Electroencephalography
  • Machine learning
  • Pattern classification

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)


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