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

Binghamton University

State University of New York Binghamton University

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

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
State	Published - 2015
Event	8th International Conference on Brain Informatics and Health, BIH 2015 - London, United Kingdom Duration: Aug 30 2015 → Sep 2 2015

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	9250

Conference

Conference	8th International Conference on Brain Informatics and Health, BIH 2015
Country/Territory	United Kingdom
City	London
Period	08/30/15 → 09/2/15

Keywords

Adolescence
Brain development
Electroencephalography
Machine learning
Pattern classification

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

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)).

@inproceedings{caef3c6972fc479ca5091bbf34b04264,

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.",

keywords = "Adolescence, Brain development, Electroencephalography, Machine learning, Pattern classification",

author = "Miaolin Fan and Vladimir Miskovic and Chou, \{Chun An\} and Sina Khanmohammadi and Hiroki Sayama and Gibb, \{Brandon E.\}",

note = "Publisher Copyright: {\textcopyright} Springer International Publishing Switzerland 2015.; 8th International Conference on Brain Informatics and Health, BIH 2015 ; Conference date: 30-08-2015 Through 02-09-2015",

year = "2015",

doi = "10.1007/978-3-319-23344-4\_10",

language = "English",

isbn = "9783319233437",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer Verlag",

pages = "96--104",

editor = "\{Guo Y.\}, Yike and \{Hill S.\}, Sean and Karl Friston and Hanchuan Peng and \{Faisal A.\}, Aldo",

booktitle = "Brain Informatics and Health - 8th International Conference, BIH 2015, Proceedings",

}

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, 08/30/15. 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 › peer-review

TY - GEN

T1 - Classification analysis of chronological age using brief resting electroencephalographic (EEG) recordings

AU - Fan, Miaolin

AU - Miskovic, Vladimir

AU - Chou, Chun An

AU - Khanmohammadi, Sina

AU - Sayama, Hiroki

AU - Gibb, Brandon E.

N1 - Publisher Copyright: © Springer International Publishing Switzerland 2015.

PY - 2015

Y1 - 2015

N2 - 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.

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.

KW - Adolescence

KW - Brain development

KW - Electroencephalography

KW - Machine learning

KW - Pattern classification

UR - https://www.scopus.com/pages/publications/84945924169

U2 - 10.1007/978-3-319-23344-4_10

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

M3 - Conference contribution

SN - 9783319233437

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 96

EP - 104

BT - Brain Informatics and Health - 8th International Conference, BIH 2015, Proceedings

A2 - Guo Y., Yike

A2 - Hill S., Sean

A2 - Friston, Karl

A2 - Peng, Hanchuan

A2 - Faisal A., Aldo

PB - Springer Verlag

T2 - 8th International Conference on Brain Informatics and Health, BIH 2015

Y2 - 30 August 2015 through 2 September 2015

ER -

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

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

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