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

doi:10.1007/978-3-319-23344-4_10

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 proceeding › Conference contribution

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.

Original language	English
Title of host publication	Brain Informatics and Health - 8th International Conference, BIH 2015, Proceedings
Editors	Yike Guo Y., Sean Hill S., Karl Friston, Hanchuan Peng, Aldo Faisal A.
Publisher	Springer Verlag
Pages	96-104
Number of pages	9
ISBN (Print)	9783319233437
DOIs	https://doi.org/10.1007/978-3-319-23344-4_10
Publication status	Published - 2015
Externally published	Yes
Event	8th International Conference on Brain Informatics and Health, BIH 2015 - London, United Kingdom Duration: 2015 Aug 30 → 2015 Sept 2

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	9250
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Other

Other	8th International Conference on Brain Informatics and Health, BIH 2015
Country/Territory	United Kingdom
City	London
Period	15/8/30 → 15/9/2

Keywords

Adolescence
Brain development
Electroencephalography
Machine learning
Pattern classification

ASJC Scopus subject areas

Theoretical Computer Science
Computer Science(all)

Access to Document

10.1007/978-3-319-23344-4_10

Cite this

Fan, M., Miskovic, V., Chou, C. A., Khanmohammadi, S., Sayama, H., & Gibb, B. E. (2015). Classification analysis of chronological age using brief resting electroencephalographic (EEG) recordings. In Y. Guo Y., S. Hill S., K. Friston, H. Peng, & A. Faisal A. (Eds.), Brain Informatics and Health - 8th International Conference, BIH 2015, Proceedings (pp. 96-104). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9250). Springer Verlag. https://doi.org/10.1007/978-3-319-23344-4_10

Classification analysis of chronological age using brief resting electroencephalographic (EEG) recordings. / Fan, Miaolin; Miskovic, Vladimir; Chou, Chun An et al.

Brain Informatics and Health - 8th International Conference, BIH 2015, Proceedings. ed. / Yike Guo Y.; Sean Hill S.; Karl Friston; Hanchuan Peng; Aldo Faisal A. Springer Verlag, 2015. p. 96-104 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9250).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Fan, M, Miskovic, V, Chou, CA, Khanmohammadi, S, Sayama, H & Gibb, BE 2015, Classification analysis of chronological age using brief resting electroencephalographic (EEG) recordings. in Y Guo Y., S Hill S., K Friston, H Peng & A Faisal A. (eds), Brain Informatics and Health - 8th International Conference, BIH 2015, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9250, Springer Verlag, pp. 96-104, 8th International Conference on Brain Informatics and Health, BIH 2015, London, United Kingdom, 15/8/30. https://doi.org/10.1007/978-3-319-23344-4_10

Fan M, Miskovic V, Chou CA, Khanmohammadi S, Sayama H, Gibb BE. Classification analysis of chronological age using brief resting electroencephalographic (EEG) recordings. In Guo Y. Y, Hill S. S, Friston K, Peng H, Faisal A. A, editors, Brain Informatics and Health - 8th International Conference, BIH 2015, Proceedings. Springer Verlag. 2015. p. 96-104. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-23344-4_10

Fan, Miaolin ; Miskovic, Vladimir ; Chou, Chun An et al. / Classification analysis of chronological age using brief resting electroencephalographic (EEG) recordings. Brain Informatics and Health - 8th International Conference, BIH 2015, Proceedings. editor / Yike Guo Y. ; Sean Hill S. ; Karl Friston ; Hanchuan Peng ; Aldo Faisal A. Springer Verlag, 2015. pp. 96-104 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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AU - Gibb, Brandon E.

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AB - 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.

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

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Keywords

ASJC Scopus subject areas

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