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

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

Emotion Detection Using Noninvasive Low Cost Sensors

Emotion recognition from biometrics is relevant to a wide range of application domains, including healthcare. Existing approaches usually adopt multi-electrodes sensors that could be expensive or uncomfortable to be used in real-life situations. In this study, we investigate whether we can reliably recognize high vs. low emotional valence and arousal by relying on noninvasive low cost EEG, EMG, and GSR sensors. We report the results of an empirical study involving 19 subjects. We achieve state-of-the- art classification performance for both valence and arousal even in a cross-subject classification setting, which eliminates the need for individual training and tuning of classification models.Comment: To appear in Proceedings of ACII 2017, the Seventh International Conference on Affective Computing and Intelligent Interaction, San Antonio, TX, USA, Oct. 23-26, 201

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

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

Optimal set of EEG features for emotional state classification and trajectory visualization in Parkinson's disease

In addition to classic motor signs and symptoms, individuals with Parkinson's disease (PD) are characterized by emotional deficits. Ongoing brain activity can be recorded by electroencephalograph (EEG) to discover the links between emotional states and brain activity. This study utilized machine-learning algorithms to categorize emotional states in PD patients compared with healthy controls (HC) using EEG. Twenty non-demented PD patients and 20 healthy age-, gender-, and education level-matched controls viewed happiness, sadness, fear, anger, surprise, and disgust emotional stimuli while fourteen-channel EEG was being recorded. Multimodal stimulus (combination of audio and visual) was used to evoke the emotions. To classify the EEG-based emotional states and visualize the changes of emotional states over time, this paper compares four kinds of EEG features for emotional state classification and proposes an approach to track the trajectory of emotion changes with manifold learning. From the experimental results using our EEG data set, we found that (a) bispectrum feature is superior to other three kinds of features, namely power spectrum, wavelet packet and nonlinear dynamical analysis; (b) higher frequency bands (alpha, beta and gamma) play a more important role in emotion activities than lower frequency bands (delta and theta) in both groups and; (c) the trajectory of emotion changes can be visualized by reducing subject-independent features with manifold learning. This provides a promising way of implementing visualization of patient's emotional state in real time and leads to a practical system for noninvasive assessment of the emotional impairments associated with neurological disorders

Neural mechanisms of affective instability and cognitive control in substance use

Objective: We explored the impact of affect on cognitive control as this relates to individual differences in affective instability and substance use. Toward this end, we examined how different dimensions of affective instability interact to predict substance misuse and the effect of this on two event-related potential components, the reward positivity and the late positive potential, which are said to reflect the neural mechanisms of reward and emotion processing, respectively. Methods: We recorded the ongoing electroencephalogram from undergraduate students as they navigated two T-maze tasks in search of rewards. One of the tasks included neutral, pleasant, and unpleasant pictures from the International Affective Picture System. Participants also completed several questionnaires pertaining to substance use and personality. Results: A principal components analysis revealed a factor related to affective instability, which we named reactivity. This factor significantly predicted increased substance use. Individuals reporting higher levels of affective reactivity also displayed a larger reward positivity following stimuli with emotional content. Conclusion: The current study uncovered a group of high-risk substance users who were characterized by greater levels of affective reactivity and context-specific increased sensitivity to rewards. Significance: These results help to elucidate the complex factors underlying substance use and may facilitate the creation of individually-tailored treatment programs for those struggling with substance use disorders

How Does the Body Affect the Mind? Role of Cardiorespiratory Coherence in the Spectrum of Emotions

The brain is considered to be the primary generator and regulator of emotions; however, afferent signals originating throughout the body are detected by the autonomic nervous system (ANS) and brainstem, and, in turn, can modulate emotional processes. During stress and negative emotional states, levels of cardiorespiratory coherence (CRC) decrease, and a shift occurs toward sympathetic dominance. In contrast, CRC levels increase during more positive emotional states, and a shift occurs toward parasympathetic dominance. Te dynamic changes in CRC that accompany different emotions can provide insights into how the activity of the limbic system and afferent feedback manifest as emotions. The authors propose that the brainstem and CRC are involved in important feedback mechanisms that modulate emotions and higher cortical areas. That mechanism may be one of many mechanisms that underlie the physiological and neurological changes that are experienced during pranayama and meditation and may support the use of those techniques to treat various mood disorders and reduce stress