Perception of emotional speech in Parkinson's Disease

Schröder, Christine; Möbes, J.; Schütze, Martin; Szymanowski, F.; Nager, Wido; Bangert, Marc; Münte, Thomas F.; Dengler, Reinhard

doi:10.1002/mds.21038

Cited by 63 publications

(53 citation statements)

References 28 publications

(32 reference statements)

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“…Auditory ERP paradigms have also been used to investigate PD patients' perception of emotional speech. By using sadly spoken and happily spoken target words in an oddball procedure, Schröder et al (2006) were able to show that patients had difficulties discriminating emotional prosody. This performance decrement was also mirrored on the level of the ERPs with decreased P3b amplitude in PD patients for happy but not for sad targets.…”

Section: Emotionmentioning

confidence: 99%

Event-related potentials and cognition in Parkinson’s disease: An integrative review

Seer

Lange

Georgiev

et al. 2016

Neuroscience & Biobehavioral Reviews

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Section: Emotionmentioning

confidence: 99%

Event-related potentials and cognition in Parkinson’s disease: An integrative review

Seer

Lange

Georgiev

et al. 2016

Neuroscience & Biobehavioral Reviews

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“…Exclusion criteria for both groups were dementia or depression as indicated by a score of 24 or lower on the Mini-Mental State Examination (MMSE) (Folstein et al, 1975;Wieser et al, 2012) or 18 or higher on the Beck Depression Inventory (BDI) (Beck et al, 1961;Schröder et al, 2006). All participants were right-handed as determined by self-report and confirmed by Edinburgh Handedness Inventory (EHI) (Oldfield, 1971).…”

Section: Recruitment Of Eligible Participantsmentioning

confidence: 99%

“…Furthermore, there is sparse event related potential (ERP) evidence that early processing of emotional prosody (mismatch negativity, Schröder et al, 2006) and faces (early posterior negativity, Wieser et al, 2012) may be affected in PD. Altogether, experimental evidence does support the view of deficits in emotion processing in PD patients.…”

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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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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“…There is sparse event related potential (ERP) evidence that early processing of emotional prosody (mismatch negativity, [15]) and faces (show reduced arousal ratings of highly arousing affective pictures, [16] and early posterior negativity [17]) may be affected in PD. Still there is some controversy about which specific emotions are recognized abnormally in PD.…”

Section: Introductionmentioning

confidence: 99%

“…In general, EEG signals have been widely used in order to study brain activity relating to affective responses. Evidence of such activity is reported in the majority of EEG frequency bands such as theta (4)(5)(6)(7)(8), alpha (8 -13 Hz), beta (13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30) and gamma (30)(31)(32)(33)(34)(35)(36)(37)(38)(39)(40)(41)(42)(43)(44)(45)(46)(47)(48)(49). For example, frontal midline (Fm) theta power modulation is suggested to reflect affective processing during emotional music [27].…”

mentioning

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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Perception of emotional speech in Parkinson's Disease

Cited by 63 publications

References 28 publications

Event-related potentials and cognition in Parkinson’s disease: An integrative review

Event-related potentials and cognition in Parkinson’s disease: An integrative review

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

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

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