Support vector machine for classification of stress subjects using EEG signals

Author

Sani, M.M. ; Norhazman, H. ; Omar, H.A. ; Zaini, Norliza ; Ghani, S.A.

Author_Institution

Center for Comput. Eng. Studies, Univ. of Technol. Mara, Shah Alam, Malaysia

fYear

2014

Firstpage

127

Lastpage

131

Abstract

Stress is a mental condition that can effects the brain electrical activity to be different from the normal state. This brain cognitive change can be measured using EEG. The objective of this paper is to classify stress subjects based on EEG signal using SVM. The data which are used to represent stress subjects were taken from the residents of Pusat Darul Wardah; a shelter centre for troubled women. SVM is used to classify the EEG Alpha band data for Power Spectral Density and Energy Spectral Density. Using 5-fold cross validation, the classification rate are 83.33% for ESD data using RBF kernel function.

Keywords

electroencephalography; medical signal processing; signal classification; support vector machines; EEG signal; SVM; brain cognitive change; brain electrical activity; energy spectral density; mental health; power spectral density; stress subject classification; support vector machine; Accuracy; Conferences; Electroencephalography; Electrostatic discharges; Kernel; Stress; Support vector machines; EEG signal; Radial Basis Function; SVM; Stress-detection;

fLanguage

English

Publisher

ieee

Conference_Titel

Systems, Process and Control (ICSPC), 2014 IEEE Conference on

Print_ISBN

978-1-4799-6105-4

Type

conf

DOI

10.1109/SPC.2014.7086243

Filename

7086243