Detection of emotions in Parkinson's disease using higher order spectral features from brain's electrical activity

R. Yuvaraj, M. Murugappan, Norlinah Mohamed Ibrahim, Kenneth Sundaraj, Mohd Iqbal Omar, Khairiyah Mohamad, R. Palaniappan

Research output: Contribution to journalArticle

16 Citations (Scopus)

Abstract

Objective Non-motor symptoms in Parkinson's disease (PD) involving cognition and emotion have been progressively receiving more attention in recent times. Electroencephalogram (EEG) signals, being an activity of central nervous system, can reflect the underlying true emotional state of a person. This paper presents a computational framework for classifying PD patients compared to healthy controls (HC) using emotional information from the brain's electrical activity. Approach Emotional EEG data were obtained from 20 PD patients and 20 healthy age-, gender- and education level-matched controls by inducing the six basic emotions of happiness, sadness, fear, anger, surprise and disgust using multimodal (audio and visual) stimuli. In addition, participants were asked to report their subjective affect. Because of the nonlinear and dynamic nature of EEG signals, we utilized higher order spectral features (specifically, bispectrum) for analysis. Two different classifiers namely K-Nearest Neighbor (KNN) and Support Vector Machine (SVM) were used to investigate the performance of the HOS based features to classify each of the six emotional states of PD patients compared to HC. Ten-fold cross-validation method was used for testing the reliability of the classifier results. Main results From the experimental results with our EEG data set, we found that (a) classification performance of bispectrum features across ALL frequency bands is better than individual frequency bands in both the groups using SVM classifier; (b) higher frequency band plays a more important role in emotion activities than lower frequency band; and (c) PD patients showed emotional impairments compared to HC, as demonstrated by a lower classification performance, particularly for negative emotions (sadness, fear, anger and disgust). Significance These results demonstrate the effectiveness of applying EEG features with machine learning techniques to classify the each emotional state difference of PD patients compared to HC, and offer a promising approach for detection of emotional impairments associated with other neurological disorders.

Original languageEnglish
Pages (from-to)108-116
Number of pages9
JournalBiomedical Signal Processing and Control
Volume14
Issue number1
DOIs
Publication statusPublished - 2014

Fingerprint

Parkinson Disease
Electroencephalography
Brain
Emotions
Frequency bands
Classifiers
Anger
Fear
Support vector machines
Happiness
Nonlinear Dynamics
Neurology
Nervous System Diseases
Reproducibility of Results
Cognition
Learning systems
Central Nervous System
Education
Testing
Support Vector Machine

Keywords

  • Bispectrum
  • Electroencephalogram
  • Emotion recognition
  • Parkinson's disease
  • Support vector machine

ASJC Scopus subject areas

  • Health Informatics
  • Signal Processing

Cite this

Detection of emotions in Parkinson's disease using higher order spectral features from brain's electrical activity. / Yuvaraj, R.; Murugappan, M.; Mohamed Ibrahim, Norlinah; Sundaraj, Kenneth; Omar, Mohd Iqbal; Mohamad, Khairiyah; Palaniappan, R.

In: Biomedical Signal Processing and Control, Vol. 14, No. 1, 2014, p. 108-116.

Research output: Contribution to journalArticle

Yuvaraj, R. ; Murugappan, M. ; Mohamed Ibrahim, Norlinah ; Sundaraj, Kenneth ; Omar, Mohd Iqbal ; Mohamad, Khairiyah ; Palaniappan, R. / Detection of emotions in Parkinson's disease using higher order spectral features from brain's electrical activity. In: Biomedical Signal Processing and Control. 2014 ; Vol. 14, No. 1. pp. 108-116.
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