Yuvaraj R, Murugappan M, Ibrahim Norlinah Mohamed, Omar Mohd Iqbal, Sundaraj Kenneth, Mohamad Khairiyah, Palaniappan R, Satiyan M
School of Mechatronic Engineering, University Malaysia Perlis (UniMAP), Malaysia.
J Integr Neurosci. 2014 Mar;13(1):89-120. doi: 10.1142/S021963521450006X. Epub 2014 Mar 11.
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.
处理情绪能力的缺陷是几种神经精神疾病的特征，也是帕金森病（PD）的特点，因此需要一种量化情绪的方法，目前情绪量化是通过临床诊断来进行的。脑电图（EEG）信号作为中枢神经系统（CNS）的一种活动，能够反映一个人潜在的真实情绪状态。本研究应用机器学习算法对帕金森病患者的脑电情绪状态进行分类，与健康对照（HC）相比，该算法将对六种基本情绪（快乐、悲伤、恐惧、愤怒、惊讶和厌恶）进行分类。使用多模态（视听）刺激，记录了20名帕金森病患者和20名年龄、教育水平和性别匹配的健康对照的情绪脑电数据。据报道，由高阶谱（HOS）激发的非线性特征的使用是一种对情绪状态进行分类的有前景的方法。在这项工作中，我们使用从高阶谱和功率谱导出的特征，对k近邻（kNN）和支持向量机（SVM）分类器的性能进行了比较研究。方差分析（ANOVA）表明，基于功率谱和高阶谱的特征在六种情绪状态之间具有统计学显著性（p < 0.0001）。分类结果表明，使用基于所选高阶谱的特征而非基于功率谱的特征，对于所有六个类别都能提供相对更好的准确率，使用支持向量机分类器时，帕金森病患者和健康对照在β（13 - 30 Hz）频段的总体准确率分别为70.10% ± 2.83%和77.29% ± 1.73%。此外，与健康对照相比，帕金森病患者在处理负面情绪（悲伤、恐惧、愤怒和厌恶）时的准确率低于处理正面情绪（快乐、惊讶）时的准确率。这些结果证明了以用户独立的方式，将机器学习技术应用于利用脑电信号对帕金森病患者的情绪状态进行分类的有效性。通过研究其他基于高阶谱的特征，可以提高系统的准确率。本研究可能会促成一个用于无创评估与神经疾病相关的情绪障碍的实用系统。
