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

2014; Imperial College Press; Volume: 13; Issue: 01 Linguagem: Inglês

10.1142/s021963521450006x

1757-448X

Rajamanickam Yuvaraj, M. Murugappan, Norlinah Mohamed Ibrahim, Mohammad Iqbal Omar, Kenneth Sundaraj, Khairiyah Mohamad, Ramaswamy Palaniappan, M. Satiyan,

Parkinson's Disease Mechanisms and Treatments

Journal of Integrative NeuroscienceVol. 13, No. 01, pp. 89-120 (2014) ArticlesNo AccessEmotion classification in Parkinson's disease by higher-order spectra and power spectrum features using EEG signals: A comparative studyR. Yuvaraj, M. Murugappan, Norlinah Mohamed Ibrahim, Mohd Iqbal Omar, Kenneth Sundaraj, Khairiyah Mohamad, R. Palaniappan, and M. SatiyanR. YuvarajSchool of Mechatronic Engineering, University Malaysia Perlis (UniMAP), Malaysia, M. MurugappanSchool of Mechatronic Engineering, University Malaysia Perlis (UniMAP), Malaysia, Norlinah Mohamed IbrahimNeurology Unit, Department of Medicine, UKM Medical Center, Kuala Lumpur, Malaysia, Mohd Iqbal OmarSchool of Mechatronic Engineering, University Malaysia Perlis (UniMAP), Malaysia, Kenneth SundarajSchool of Mechatronic Engineering, University Malaysia Perlis (UniMAP), Malaysia, Khairiyah MohamadNeurology Unit, Department of Medicine, UKM Medical Center, Kuala Lumpur, Malaysia, R. PalaniappanFaculty of Science and Engineering, University of Wolverhampton, United Kingdom, and M. SatiyanSchool of Mechatronic Engineering, University Malaysia Perlis (UniMAP), Malaysiahttps://doi.org/10.1142/S021963521450006XCited by:30 PreviousNext AboutSectionsPDF/EPUB ToolsAdd to favoritesDownload CitationsTrack CitationsRecommend to Library ShareShare onFacebookTwitterLinked InRedditEmail 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. 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