TY - JOUR
T1 - Emotion Recognition Based on DEAP Database using EEG Time-Frequency Features and Machine Learning Methods
AU - Kusumaningrum, T. D.
AU - Faqih, A.
AU - Kusumoputro, B.
N1 - Publisher Copyright:
© Published under licence by IOP Publishing Ltd.
PY - 2020/5/28
Y1 - 2020/5/28
N2 - 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%.
AB - 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%.
UR - http://www.scopus.com/inward/record.url?scp=85086505105&partnerID=8YFLogxK
U2 - 10.1088/1742-6596/1501/1/012020
DO - 10.1088/1742-6596/1501/1/012020
M3 - Conference article
AN - SCOPUS:85086505105
VL - 1501
JO - Journal of Physics: Conference Series
JF - Journal of Physics: Conference Series
SN - 1742-6588
IS - 1
M1 - 012020
T2 - 2019 International Conference on Science and Technology, ICoST 2019
Y2 - 2 November 2019 through 3 November 2019
ER -