Psychiatry Investig.  2018 Aug;15(8):790-795. 10.30773/pi.2018.04.03.1.

Predicting Working Memory Capacity in Older Subjects Using Quantitative Electroencephalography

• Shin JH
• Jhung K
• Heo JS
• An SK
• Park JY

• KMID: 2419368
• DOI: http://doi.org/10.30773/pi.2018.04.03.1

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.

Keyword

Older subjects; Working memory; Support vector machine; Spectral analysis; Brain connectivitys

MeSH Terms

Aged
Brain
Classification
Cognition
Electroencephalography*
Humans
Memory, Short-Term*
Methods
Sensitivity and Specificity
Support Vector Machine