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 |
| Publisher | Springer Verlag |
| Pages | 96-104 |
| Number of pages | 9 |
| Volume | 9250 |
| ISBN (Print) | 9783319233437 |
| DOIs | |
| 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 Sep 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 | United Kingdom |
| City | London |
| Period | 15/8/30 → 15/9/2 |
Fingerprint
Keywords
- Adolescence
- Brain development
- Electroencephalography
- Machine learning
- Pattern classification
ASJC Scopus subject areas
- Theoretical Computer Science
- Computer Science(all)
Cite this
Classification analysis of chronological age using brief resting electroencephalographic (EEG) recordings. / Fan, Miaolin; Miskovic, Vladimir; Chou, Chun An; Khanmohammadi, Sina; Sayama, Hiroki; Gibb, Brandon E.
Brain Informatics and Health - 8th International Conference, BIH 2015, Proceedings. Vol. 9250 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
}
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.
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 - http://www.scopus.com/inward/record.url?scp=84945924169&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84945924169&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-23344-4_10
DO - 10.1007/978-3-319-23344-4_10
M3 - Conference contribution
AN - SCOPUS:84945924169
SN - 9783319233437
VL - 9250
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
PB - Springer Verlag
ER -