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Emotion classification in Parkinson's disease by higher-order spectra and power spectrum features using EEG signals: a comparative study

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Main Authors: Yuvaraj, Rajamanickam, Murugappan, M., Dr., Norlinah, Mohamed Ibrahim, Mohammad Iqbal, Omar@Ye Htut, Assoc. Prof. Dr., Sundaraj, Kenneth, Prof. Dr., Khairiyah, Mohamad, Palaniappan, Ramaswamy, Satiyan, Marimuthu
Other Authors: yuva2257@gmail.com
Format: Article
Language:English
Published: World Scientific Publishing Co. Pte Ltd 2014
Subjects:
Bispectrum
EEG
Emotion
Parkinson's disease
Pattern classification
Power spectrum
Online Access:http://dspace.unimap.edu.my:80/dspace/handle/123456789/34543
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spelling my.unimap-345432014-05-20T03:49:11Z Emotion classification in Parkinson's disease by higher-order spectra and power spectrum features using EEG signals: a comparative study Yuvaraj, Rajamanickam Murugappan, M., Dr. Norlinah, Mohamed Ibrahim Mohammad Iqbal, Omar@Ye Htut, Assoc. Prof. Dr. Sundaraj, Kenneth, Prof. Dr. Khairiyah, Mohamad Palaniappan, Ramaswamy Satiyan, Marimuthu yuva2257@gmail.com murugappan@unimap.edu.my norlinah@ppukm.ukm.my iqbalomar@unimap.edu.my kenneth@unimap.edu.my plumfield82@yahoo.com palani@wlv.ac.uk msatiyan316@gmail.com Bispectrum EEG Emotion Parkinson's disease Pattern classification Power spectrum Link to publisher's homepage at http://www.worldscientific.com/ Deficits 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. 2014-05-20T03:49:11Z 2014-05-20T03:49:11Z 2014-03 Article Journal of Integrative Neuroscience, vol. 13(1), 2014, pages 89-120 0219-6352 http://www.worldscientific.com/doi/abs/10.1142/S021963521450006X? http://dspace.unimap.edu.my:80/dspace/handle/123456789/34543 10.1142/S021963521450006X en World Scientific Publishing Co. Pte Ltd
institution Universiti Malaysia Perlis
building UniMAP Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Perlis
content_source UniMAP Library Digital Repository
url_provider http://dspace.unimap.edu.my/
language English
topic Bispectrum
EEG
Emotion
Parkinson's disease
Pattern classification
Power spectrum
spellingShingle Bispectrum
EEG
Emotion
Parkinson's disease
Pattern classification
Power spectrum
Yuvaraj, Rajamanickam
Murugappan, M., Dr.
Norlinah, Mohamed Ibrahim
Mohammad Iqbal, Omar@Ye Htut, Assoc. Prof. Dr.
Sundaraj, Kenneth, Prof. Dr.
Khairiyah, Mohamad
Palaniappan, Ramaswamy
Satiyan, Marimuthu
Emotion classification in Parkinson's disease by higher-order spectra and power spectrum features using EEG signals: a comparative study
description Link to publisher's homepage at http://www.worldscientific.com/
author2 yuva2257@gmail.com
author_facet yuva2257@gmail.com
Yuvaraj, Rajamanickam
Murugappan, M., Dr.
Norlinah, Mohamed Ibrahim
Mohammad Iqbal, Omar@Ye Htut, Assoc. Prof. Dr.
Sundaraj, Kenneth, Prof. Dr.
Khairiyah, Mohamad
Palaniappan, Ramaswamy
Satiyan, Marimuthu
format Article
author Yuvaraj, Rajamanickam
Murugappan, M., Dr.
Norlinah, Mohamed Ibrahim
Mohammad Iqbal, Omar@Ye Htut, Assoc. Prof. Dr.
Sundaraj, Kenneth, Prof. Dr.
Khairiyah, Mohamad
Palaniappan, Ramaswamy
Satiyan, Marimuthu
author_sort Yuvaraj, Rajamanickam
title Emotion classification in Parkinson's disease by higher-order spectra and power spectrum features using EEG signals: a comparative study
title_short Emotion classification in Parkinson's disease by higher-order spectra and power spectrum features using EEG signals: a comparative study
title_full Emotion classification in Parkinson's disease by higher-order spectra and power spectrum features using EEG signals: a comparative study
title_fullStr Emotion classification in Parkinson's disease by higher-order spectra and power spectrum features using EEG signals: a comparative study
title_full_unstemmed Emotion classification in Parkinson's disease by higher-order spectra and power spectrum features using EEG signals: a comparative study
title_sort emotion classification in parkinson's disease by higher-order spectra and power spectrum features using eeg signals: a comparative study
publisher World Scientific Publishing Co. Pte Ltd
publishDate 2014
url http://dspace.unimap.edu.my:80/dspace/handle/123456789/34543
_version_ 1643797516766937088
score 13.214268

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