Abstract

In recent years, research of the human emotional state is becoming importance, especially in its application for patient monitoring and in the treatment management system of that patient. In this paper, an EEG based emotion recognition system is developed that consists of a feature extraction subsystem and a classifier subsystem. As better performance of the feature extraction subsystem may produce higher recognition accuracy, nine features derived from the time and frequency domain from the EEG signal is used and analyzed. We have utilized support vector machine and Random Forest methods for classifying the emotional state of the subject, and compare its results with other machine learning methods. Using two-fold data validation model, the experiment result shows that the highest recognition accuracy is produced by using Random Forest method, i.e., 62.58%.

Details

Title
Emotion Recognition Based on DEAP Database using EEG Time-Frequency Features and Machine Learning Methods
Author
Kusumaningrum, T D 1 ; Faqih, A 1 ; Kusumoputro, B 1 

 Dept of Electrical Engineering Universitas Indonesia 
Publication year
2020
Publication date
Mar 2020
Publisher
IOP Publishing
ISSN
17426588
e-ISSN
17426596
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2569791200
Copyright
© 2020. This work is published under http://creativecommons.org/licenses/by/3.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.