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dc.contributor.authorYuvaraj, Rajamanickam
dc.contributor.authorMurugappan, M., Dr.
dc.contributor.authorNorlinah, Mohamed Ibrahim
dc.contributor.authorMohammad Iqbal, Omar@Ye Htut, Assoc. Prof. Dr.
dc.contributor.authorSundaraj, Kenneth, Prof. Dr.
dc.contributor.authorKhairiyah, Mohamad
dc.contributor.authorPalaniappan, Ramaswamy
dc.contributor.authorSatiyan, Marimuthu
dc.date.accessioned2014-05-20T03:49:11Z
dc.date.available2014-05-20T03:49:11Z
dc.date.issued2014-03
dc.identifier.citationJournal of Integrative Neuroscience, vol. 13(1), 2014, pages 89-120en_US
dc.identifier.issn0219-6352
dc.identifier.urihttp://www.worldscientific.com/doi/abs/10.1142/S021963521450006X?
dc.identifier.urihttp://dspace.unimap.edu.my:80/dspace/handle/123456789/34543
dc.descriptionLink to publisher's homepage at http://www.worldscientific.com/en_US
dc.description.abstractDeficits in the ability to process emotions characterize several neuropsychiatric disorders and are traits of Parkinson's disease (PD), and there is need for a method of quantifying emotion, which is currently performed by clinical diagnosis. Electroencephalogram (EEG) signals, being an activity of central nervous system (CNS), can reflect the underlying true emotional state of a person. This study applied machine-learning algorithms to categorize EEG emotional states in PD patients that would classify six basic emotions (happiness and sadness, fear, anger, surprise and disgust) in comparison with healthy controls (HC). Emotional EEG data were recorded from 20 PD patients and 20 healthy age-, education level-and sex-matched controls using multimodal (audio-visual) stimuli. The use of nonlinear features motivated by the higher-order spectra (HOS) has been reported to be a promising approach to classify the emotional states. In this work, we made the comparative study of the performance of k-nearest neighbor (kNN) and support vector machine (SVM) classifiers using the features derived from HOS and from the power spectrum. Analysis of variance (ANOVA) showed that power spectrum and HOS based features were statistically significant among the six emotional states (p < 0.0001). Classification results shows that using the selected HOS based features instead of power spectrum based features provided comparatively better accuracy for all the six classes with an overall accuracy of 70.10% ± 2.83% and 77.29% ± 1.73% for PD patients and HC in beta (13-30 Hz) band using SVM classifier. Besides, PD patients achieved less accuracy in the processing of negative emotions (sadness, fear, anger and disgust) than in processing of positive emotions (happiness, surprise) compared with HC. These results demonstrate the effectiveness of applying machine learning techniques to the classification of emotional states in PD patients in a user independent manner using EEG signals. The accuracy of the system can be improved by investigating the other HOS based features. This study might lead to a practical system for noninvasive assessment of the emotional impairments associated with neurological disorders.en_US
dc.language.isoenen_US
dc.publisherWorld Scientific Publishing Co. Pte Ltden_US
dc.subjectBispectrumen_US
dc.subjectEEGen_US
dc.subjectEmotionen_US
dc.subjectParkinson's diseaseen_US
dc.subjectPattern classificationen_US
dc.subjectPower spectrumen_US
dc.titleEmotion classification in Parkinson's disease by higher-order spectra and power spectrum features using EEG signals: a comparative studyen_US
dc.typeArticleen_US
dc.identifier.url10.1142/S021963521450006X
dc.contributor.urlyuva2257@gmail.comen_US
dc.contributor.urlmurugappan@unimap.edu.myen_US
dc.contributor.urlnorlinah@ppukm.ukm.myen_US
dc.contributor.urliqbalomar@unimap.edu.myen_US
dc.contributor.urlkenneth@unimap.edu.myen_US
dc.contributor.urlplumfield82@yahoo.comen_US
dc.contributor.urlpalani@wlv.ac.uken_US
dc.contributor.urlmsatiyan316@gmail.comen_US


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