Emotional processing in Parkinson's disease: A systematic review

Péron, Julie Anne; Dondaine, Thibaut; Jeune, Florence Le; Grandjean, Didier; Vérin, Marc

doi:10.1002/mds.24025

Cited by 159 publications

(165 citation statements)

References 105 publications

(101 reference statements)

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“…In works related to emotion recognition using physiological signal, acquiring emotional data that corresponds to specific emotional state is challenging, because of the subjective nature of the emotions and cognitive dependence of physiological signals which requires the emotional states to be elicited internally in the participant. Until now, most studies on emotion recognition in PD have used only facial stimuli, prosodic stimuli, or music stimuli (Gray and Tickle-Degnen, 2010;Lima et al, 2013;Péron et al, 2012). In addition, a number of emotion induction techniques Emotional state classification in PD 10 using pictures, sounds, music, or multimodal approach (combination of audio and visual) have been used to elicit the target emotions in healthy controls (Baumgartner et al, 2006;Lin et al, 2010;Murugappan et al, 2010;Petrantonakis and Hadjileontiadis, 2011;Wang et al, 2013).…”

Section: Stimulus Materialsmentioning

confidence: 99%

“…Over the last decade, there has been an increasing attention to the role played by emotional processes in PD (Gray and Tickle-Degnen, 2010;Péron et al, 2012). Evidence indicates the individuals with PD have deficits in recognizing emotions from prosody (Dara et al, 2008;Pell and Leonard, 2003;Yip et al, 2003) and facial expressions (Ariatti et al, 2008;Clark et al, 2008;Dujardin et al, 2004), although not all findings have been consistent.…”

Section: Introductionmentioning

confidence: 99%

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

Yuvaraj

Murugappan

Ibrahim

et al. 2014

International Journal of Psychophysiology

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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 as 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.Multi-modal 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 the 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.

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Section: Stimulus Materialsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Yuvaraj

Murugappan

Ibrahim

et al. 2014

International Journal of Psychophysiology

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“…These lead to difficulties in describing bodily sensations, physiological arousal and feelings, expressing emotions, and identifying others' emotions from prosody and facial expression 8, 9, 10. Facial emotion recognition (FER) is one of the most basic aspects of emotional functioning and one of the most critical components of social behaviors.…”

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confidence: 99%

“…However, studies assessing FER in PD still report contradictory results. Although 2 reviews and 1 meta‐analysis revealed an FER deficit in PD and outlined potential biasing factors,9, 11, 12 the origins of this inconsistency are unclear, and several questions remain unanswered. For instance, what is the actual effect of dopaminergic medication on FER in PD?…”

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confidence: 99%

Facial emotion recognition in Parkinson's disease: A review and new hypotheses

et al. 2018

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Parkinson's disease is a neurodegenerative disorder classically characterized by motor symptoms. Among them, hypomimia affects facial expressiveness and social communication and has a highly negative impact on patients' and relatives' quality of life. Patients also frequently experience nonmotor symptoms, including emotional‐processing impairments, leading to difficulty in recognizing emotions from faces. Aside from its theoretical importance, understanding the disruption of facial emotion recognition in PD is crucial for improving quality of life for both patients and caregivers, as this impairment is associated with heightened interpersonal difficulties. However, studies assessing abilities in recognizing facial emotions in PD still report contradictory outcomes. The origins of this inconsistency are unclear, and several questions (regarding the role of dopamine replacement therapy or the possible consequences of hypomimia) remain unanswered. We therefore undertook a fresh review of relevant articles focusing on facial emotion recognition in PD to deepen current understanding of this nonmotor feature, exploring multiple significant potential confounding factors, both clinical and methodological, and discussing probable pathophysiological mechanisms. This led us to examine recent proposals about the role of basal ganglia‐based circuits in emotion and to consider the involvement of facial mimicry in this deficit from the perspective of embodied simulation theory. We believe our findings will inform clinical practice and increase fundamental knowledge, particularly in relation to potential embodied emotion impairment in PD. © 2018 The Authors. Movement Disorders published by Wiley Periodicals, Inc. on behalf of International Parkinson and Movement Disorder Society.

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“…Non-motor symptoms, including disruptions in processing of emotional information [4,5] have been found in over 50% of newly diagnosed PD patients [6] and can appear in any stage of disease progression [7]. Interestingly, social cognitive dysfunction has been found before the appearance of motor disturbances in PD [8].…”

Section: Introductionmentioning

confidence: 99%

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

Yuvaraj

Murugappan

Ibrahim

et al. 2014

Biomedical Signal Processing and Control

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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 BackgroundSocial communication and the ability to respond to emotional signals are essential for meaningful interpersonal interactions. While Parkinson's disease (PD) has traditionally been defined as a motor system disorder (in the form of tremors, rigidity, and bradykinesia) [1], there is growing evidence of cognitive and social deficits for people associated with this disease [2,3].Non-motor symptoms, including disruptions in processing of emotional information [4,5] have been found in over 50% of newly diagnosed PD patients [6] and can appear in any stage of disease progression [7]. Interestingly, social cognitive dysfunction has been found before the appearance of motor disturbances in PD [8].Individuals with PD show impairments in the ability to recognize emotions from facial expressions [9,10], emotional prosody [11,12] and show reduced startle reactivity to highly arousing unpleasant pictures [13,14]. There is sparse event related potential (ERP) evidence that early processing of emotional prosody (mismatch nega...

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Emotional processing in Parkinson's disease: A systematic review

Cited by 159 publications

References 105 publications

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

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

Facial emotion recognition in Parkinson's disease: A review and new hypotheses

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

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