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

*この研究の対応する著者

研究成果: Conference contribution

抄録

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.

本文言語English
ホスト出版物のタイトルBrain Informatics and Health - 8th International Conference, BIH 2015, Proceedings
出版社Springer Verlag
ページ96-104
ページ数9
9250
ISBN(印刷版)9783319233437
DOI
出版ステータスPublished - 2015
外部発表はい
イベント8th International Conference on Brain Informatics and Health, BIH 2015 - London, United Kingdom
継続期間: 2015 8月 302015 9月 2

出版物シリーズ

名前Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
9250
ISSN(印刷版)0302-9743
ISSN(電子版)1611-3349

Other

Other8th International Conference on Brain Informatics and Health, BIH 2015
国/地域United Kingdom
CityLondon
Period15/8/3015/9/2

ASJC Scopus subject areas

  • 理論的コンピュータサイエンス
  • コンピュータ サイエンス(全般)

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