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Psychiatry Investig > Epub ahead of print
[Epub ahead of print]
DOI: https://doi.org/10.30773/pi.2018.04.03.1    Published online July 4, 2018.
Predicting Working Memory Capacity in Older Subjects Using Quantitative Electroencephalography
Jae Hyuk Shin1, Kyungun Jhung2, Jae Seok Heo3,4, Suk Kyoon An3,4,5, Jin Young Park4,6
1Department of Family Medicine, Yonsei University College of Medicine, Gangnam Severance Hospital, Seoul, Republic of Korea
2Department of Psychiatry & Behavioral Neuroscience, International St. Mary’s Hospital, Catholic Kwandong University, Incheon, Republic of Korea
3The Graduate School Yonsei University Graduate Program in Cognitive Science, Seoul, Republic of Korea
4Institute of Behavioral Science in Medicine, Yonsei University College of Medicine, Seoul, Republic of Korea
5Department of Psychiatry, Yonsei University College of Medicine, Severance Hospital, Seoul, Republic of Korea
6Department of Psychiatry, Yonsei University College of Medicine, Gangnam Severance Hospital, Seoul, Republic of Korea
Correspondence: Suk Kyoon An ,Tel: +82-2-2228-1585, Fax: +82-2-313-0891, Email: ansk@yuhs.ac
Correspondence: Jin Young Park ,Tel: +82-2-2019-3341, Fax: +82-2-3462-4304, Email: empathy@yuhs.ac
Received: January 2, 2018   Revised: March 16, 2018   Accepted: April 3, 2018   Published online: July 4, 2018
Abstract
Objective
We utilized a spectral and network analysis technique with an integrated support vector classification algorithm for the automated detection of cognitive capacity using resting state electroencephalogram (EEG) signals.
Methods
An eyes-closed resting EEG was recorded in 158 older subjects, and spectral EEG parameters in seven frequency bands, as well as functional brain network parameters were, calculated. In the feature extraction stage, the statistical power of the spectral and network parameters was calculated for the low-, moderate-, and high-performance groups. Afterward, the highly-powered features were selected as input into a support vector machine classifier with two discrete outputs: low- or high-performance groups. The classifier was then trained using a training set and the performance of the classification process was evaluated using a test set.
Results
The performance of the Support Vector Machine was evaluated using a 5-fold cross-validation and area under the curve values of 70.15% and 74.06% were achieved for the letter numbering task and the spatial span task.
Conclusion
In this study, reliable results for classification accuracy and specificity were achieved. These findings provide an example of a novel method for parameter analysis, feature extraction, training, and testing the cognitive function of elderly subjects based on a quantitative EEG signal.
Key words   Older subjects, Working memory, Support vector machine, Spectral analysis, Brain connectivitys
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