relation: https://khub.utp.edu.my/scholars/5628/
title: Investigation of window size in classification of EEG-emotion signal with wavelet entropy and support vector machine
creator: Candra, H.
creator: Yuwono, M.
creator: Chai, R.
creator: Handojoseno, A.
creator: Elamvazuthi, I.
creator: Nguyen, H.T.
creator: Su, S.
description: When dealing with patients with psychological or emotional symptoms, medical practitioners are often faced with the problem of objectively recognizing their patients' emotional state. In this paper, we approach this problem using a computer program that automatically extracts emotions from EEG signals. We extend the finding of Koelstra et. al IEEE trans. affective comput., vol. 3, no. 1, pp. 18-31, 2012 using the same dataset (i.e. the DEAP: dataset for emotion analysis using electroencephalogram, physiological and video signals), where we observed that the accuracy can be further improved using wavelet features extracted from shorter time segments. More precisely, we achieved accuracy of 65% for both valence and arousal using the wavelet entropy of 3 to 12 seconds signal segments. This improvement in accuracy entails an important discovery that information on emotions contained in the EEG signal may be better described in term of wavelets and in shorter time segments. © 2015 IEEE.
publisher: Institute of Electrical and Electronics Engineers Inc.
date: 2015
type: Conference or Workshop Item
type: PeerReviewed
identifier:   Candra, H. and Yuwono, M. and Chai, R. and Handojoseno, A. and Elamvazuthi, I. and Nguyen, H.T. and Su, S.  (2015) Investigation of window size in classification of EEG-emotion signal with wavelet entropy and support vector machine.  In: UNSPECIFIED.     
relation: https://www.scopus.com/inward/record.uri?eid=2-s2.0-84953286826&doi=10.1109%2fEMBC.2015.7320065&partnerID=40&md5=b591a5835889ed726c9edb31b52bc417
relation: 10.1109/EMBC.2015.7320065
identifier: 10.1109/EMBC.2015.7320065