Rajamanickam Yuvaraj

Review of emotion recognition in stroke patients.

Objective: Patients suffering from stroke have a diminished ability to recognize emotions. This paper presents a review of neuropsychological studies that investigated the basic emotion processing deficits involved in individuals with interhemispheric brain (right, left) damage and normal controls, including processing mode (perception) and communication channels (facial, prosodic-intonational, lexical-verbal). Methods: An electronic search was conducted using specific keywords for studies investigating emotion recognition in brain damage patients. The PubMed database was searched until March 2012 as well as citations and reference lists. 92 potential articles were identified. Results: The findings showed that deficits in emotion perception were more frequently observed in individuals with right brain damage than those with left brain damage when processing facial, prosodic and lexical emotional stimuli. Conclusion: These findings suggest that the right hemisphere has a unique contribution in emotional processing and provide support for the right hemisphere emotion hypothesis. Significance: This robust deficit in emotion recognition has clinical significance. The extent of emotion recognition deficit in brain damage patients appears to be correlated with a variety of interpersonal difficulties such as complaints of frustration in social relations, feelings of social discomfort, desire to connect with others, feelings of social disconnection and use of controlling behaviors.

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 Parkinsons 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 patients emotional state in real time and leads to a practical system for noninvasive assessment of the emotional impairments associated with neurological disorders.

On the analysis of EEG power, frequency and asymmetry in Parkinson’s disease during emotion processing

ObjectiveWhile Parkinson’s disease (PD) has traditionally been described as a movement disorder, there is growing evidence of disruption in emotion information processing associated with the disease. The aim of this study was to investigate whether there are specific electroencephalographic (EEG) characteristics that discriminate PD patients and normal controls during emotion information processing.MethodEEG recordings from 14 scalp sites were collected from 20 PD patients and 30 age-matched normal controls. Multimodal (audio-visual) stimuli were presented to evoke specific targeted emotional states such as happiness, sadness, fear, anger, surprise and disgust. Absolute and relative power, frequency and asymmetry measures derived from spectrally analyzed EEGs were subjected to repeated ANOVA measures for group comparisons as well as to discriminate function analysis to examine their utility as classification indices. In addition, subjective ratings were obtained for the used emotional stimuli.ResultsBehaviorally, PD patients showed no impairments in emotion recognition as measured by subjective ratings. Compared with normal controls, PD patients evidenced smaller overall relative delta, theta, alpha and beta power, and at bilateral anterior regions smaller absolute theta, alpha, and beta power and higher mean total spectrum frequency across different emotional states. Inter-hemispheric theta, alpha, and beta power asymmetry index differences were noted, with controls exhibiting greater right than left hemisphere activation. Whereas intra-hemispheric alpha power asymmetry reduction was exhibited in patients bilaterally at all regions. Discriminant analysis correctly classified 95.0% of the patients and controls during emotional stimuli.ConclusionThese distributed spectral powers in different frequency bands might provide meaningful information about emotional processing in PD patients.

Inter-hemispheric EEG coherence analysis in Parkinson's disease: Assessing brain activity during emotion processing

Parkinson’s disease (PD) is not only characterized by its prominent motor symptoms but also associated with disturbances in cognitive and emotional functioning. The objective of the present study was to investigate the influence of emotion processing on inter-hemispheric electroencephalography (EEG) coherence in PD. Multimodal emotional stimuli (happiness, sadness, fear, anger, surprise, and disgust) were presented to 20 PD patients and 30 age-, education level-, and gender-matched healthy controls (HC) while EEG was recorded. Inter-hemispheric coherence was computed from seven homologous EEG electrode pairs (AF3–AF4, F7–F8, F3–F4, FC5–FC6, T7–T8, P7–P8, and O1–O2) for delta, theta, alpha, beta, and gamma frequency bands. In addition, subjective ratings were obtained for a representative of emotional stimuli. Interhemispherically, PD patients showed significantly lower coherence in theta, alpha, beta, and gamma frequency bands than HC during emotion processing. No significant changes were found in the delta frequency band coherence. We also found that PD patients were more impaired in recognizing negative emotions (sadness, fear, anger, and disgust) than relatively positive emotions (happiness and surprise). Behaviorally, PD patients did not show impairment in emotion recognition as measured by subjective ratings. These findings suggest that PD patients may have an impairment of inter-hemispheric functional connectivity (i.e., a decline in cortical connectivity) during emotion processing. This study may increase the awareness of EEG emotional response studies in clinical practice to uncover potential neurophysiologic abnormalities.

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 Parkinsons 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.

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

Abstract Objective Non-motor symptoms in Parkinsons 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 brains 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.

A novel Parkinson’s Disease Diagnosis Index using higher-order spectra features in EEG signals

Higher-order spectra (HOS) is an efficient feature extraction method used in various biomedical applications such as stages of sleep, epilepsy detection, cardiac abnormalities, and affective computing. The motive of this work was to explore the application of HOS for an automated diagnosis of Parkinson’s disease (PD) using electroencephalography (EEG) signals. Resting-state EEG signals collected from 20 PD patients with medication and 20 age-matched normal subjects were used in this study. HOS bispectrum features were extracted from the EEG signals. The obtained features were ranked using t value, and highly ranked features were used in order to develop the PD Diagnosis Index (PDDI). The PDDI is a single value, which can discriminate the two classes. Also, the ranked features were fed one by one to the various classifiers, namely decision tree (DT), fuzzy K-nearest neighbor (FKNN), K-nearest neighbor (KNN), naive bayes (NB), probabilistic neural network (PNN), and support vector machine (SVM), to choose the best classifier using minimum number of features. We have obtained an optimum mean classification accuracy of 99.62%, mean sensitivity and specificity of 100.00 and 99.25%, respectively, using the SVM classifier. The proposed PDDI can aid the clinicians in their diagnosis and help to test the efficacy of drugs.

Emotion processing in Parkinson's disease: an EEG spectral power study.

Objective: Although an emotional deficit is a common finding in Parkinsons disease (PD), its neurobiological mechanism on emotion recognition is still unknown. This study examined the emotion processing deficits in PD patients using electroencephalogram (EEG) signals in response to multimodal stimuli. Method: EEG signals were investigated on both positive and negative emotions in 14 PD patients and 14 aged-matched normal controls (NCs). The relative power (i.e., ratio of EEG signal power in each frequency band compared to the total EEG power) was computed over three brain regions: the anterior (AF3, F7, F3, F4, F8 and AF4), central (FC5 and FC6) and posterior (T7, P7, O1, O2, P8 and T8) regions for theta (4–8 Hz), alpha (8–13 Hz), beta (13–30 Hz) and gamma (30–60 Hz) frequency sub-bands, respectively. Results: Behaviorally, PD patients showed decreased performance in classifying emotional stimuli as measured by subjective ratings. EEG power at theta, alpha, beta, and gamma bands in all regions were significantly different between the NC and PD groups during both the emotional tasks, with p-values less than 0.05. Furthermore, an increase of relative spectral powers in the theta and gamma bands and a decrease of relative powers in the alpha and beta bands were observed for PD patients compared with NCs during emotional information processing. Conclusion: The results suggest the possibility of the existence of a distinctive neurobiological substrate of PD patients during emotional information processing. Also, these distributed spectral powers in different frequency bands might provide meaningful information about emotional processing in PD patients.

Methods and approaches on emotions recognition in neurodegenerative disorders: A review

Emotion is one of the key factors for estimating the human behavior through verbal or non-verbal communication. Development of emotion recognition system gains a major attention in several applications, mainly in brain computer interface (BCI). Nevertheless, most of the researchers are focused on developing intelligent emotion recognition systems based on healthy subjects and no works reported on neurologic disorders (NDs). Recent years, there has been growing interest in the field of emotion recognition in NDs for improving their life style to enhance their better living. This work, presents the extensive literature study on emotion processing deficits in four common neurologic disorders (Alzheimer Disease (AD), Parkinsons Disease (PD), Huntingtons disease (HD) and Stroke) through different communication channels (facial, Prosodic, lexical). Facial expression based emotion recognition has been widely used by many researchers to identify the emotional impairment from the neurological disorder people. Results of this study indicates that, most of the NDs are subjected to the negative emotions (anger, disgust, fear and sad) impairment over positive emotions (happy and surprise).

The Effect of Lateralization of Motor Onset and Emotional Recognition in PD Patients Using EEG

The objective of this research was to investigate the relationship between emotion recognition and lateralization of motor onset in Parkinson’s disease (PD) patients using electroencephalogram (EEG) signals. The subject pool consisted of twenty PD patients [ten with predominantly left-sided (LPD) and ten with predominantly right-sided (RPD) motor symptoms] and 20 healthy controls (HC) that were matched for age and gender. Multimodal stimuli were used to evoke simple emotions, such as happiness, sadness, fear, anger, surprise, and disgust. Artifact-free emotion EEG signals were processed using the auto regressive spectral method and then subjected to repeated ANOVA measures. No group differences were observed across behavioral measures; however, a significant reduction in EEG spectral power was observed at alpha, beta and gamma frequency oscillations in LPD, compared to RPD and HC participants, suggesting that LPD patients (inferred right-hemisphere pathology) are impaired compared to RPD patients in emotional processing. We also found that PD-related emotional processing deficits may be selective to the perception of negative emotions. Previous findings have suggested a hemispheric effect on emotion processing that could be related to emotional response impairment in a subgroup of PD patients. This study may help in clinical practice to uncover potential neurophysiologic abnormalities of emotional changes with respect to PD patient’s motor onset.