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

Yuvaraj, Rajamanickam; Murugappan, M.; Ibrahim, Norlinah Mohamed; Sundaraj, Kenneth; Omar, Mohammad Iqbal; Mohamad, Khairiyah; Palaniappan, Ramaswamy

doi:10.1016/j.ijpsycho.2014.07.014

Cited by 60 publications

(46 citation statements)

References 65 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…As seen in Table 5, it can be found that the classification performance of alpha, beta, and gamma bands (i.e., high frequency bands) is better than those of delta and theta bands (i.e., low frequency bands) in LPD, RPD and HC. This finding is in line with our previous emotion studies that high frequency bands play a more important role in emotion activities than low frequency bands (Yuvaraj et al 2014b). Neuroanatomically, high frequency bands play a crucial role in integrating distributed neural processes into highly ordered cognitive functions and are important in a wide range of cognitive, perceptual, attention and emotion processes (Luo et al 2007;Sammler et al 2007).…”

Section: Resultssupporting

confidence: 91%

“…The standard frequency bands of interest were delta (1-4 Hz), theta (4-8 Hz), alpha (8-13 Hz), beta (13-30 Hz) and gamma (30-49). Each channel of the artifact-free emotional EEG data was divided into 6 s length of epochs without overlapping using time windows (Yuvaraj et al 2014b). Finally, the non-linear features were computed on each epoch of the emotional EEG data of LPD, RPD and HC participants and are subsequently explained.…”

Section: Preprocessingmentioning

confidence: 99%

“…In this work, a fractal scaling component ''a'' was used to describe the nature of emotional EEG signals. This exponent gives an indication of the short and long term correlation behaviour of signals (Yuvaraj et al 2014b). …”

Section: Detrended Fluctuation Analysis (Dfa)mentioning

confidence: 99%

“…Using non-linear kernel function, the input data is transformed to a high-dimensional feature space so that the transformed data becomes more separable than the original input data. In this work, Radial Basis Function (RBF) kernel was used (Yuvaraj et al 2014b È É . The tenfold cross validation method was used to evaluate the performance of classifiers.…”

Section: Support Vector Machine (Svm)mentioning

confidence: 99%

See 3 more Smart Citations

Hemispheric asymmetry non-linear analysis of EEG during emotional responses from idiopathic Parkinson’s disease patients

Yuvaraj¹,

Murugappan

2016

Cogn Neurodyn

Self Cite

View full text Add to dashboard Cite

Recent studies show right hemisphere has a unique contribution to emotion processing. The present study investigated EEG using non-linear measures during emotional processing in PD patients with respect to motor symptom asymmetry (i.e., most affected body side). We recorded 14-channel wireless EEGs from 20 PD patients and 10 healthy age-matched controls (HC) by eliciting emotions such as happiness, sadness, fear, anger, surprise and disgust. PD patients were divided into two groups, based on most affected body side and unilateral motor symptom severity: left side-affected (LPD, n = 10) or right side-affected PD patients (RPD, n = 10). Nonlinear analysis of these emotional EEGs were performed by using approximate entropy, correlation dimension, detrended fluctuation analysis, fractal dimension, higher order spectra, hurst exponent (HE), largest Lyapunov exponent and sample entropy. The extracted features were ranked using analysis of variance based on F value. The ranked features were then fed into classifiers namely fuzzy K-nearest neighbor and support vector machine to obtain optimal performance using minimum number of features. From the experimental results, we found that (a) classification performance across all frequency bands performed well in recognizing emotional states of LPD, RPD, and HC; (b) the emotion-specific features were mainly related to higher frequency bands; and (c) predominantly LPD patients (inferred right-hemisphere pathology) were more impaired in emotion processing compared to RPD, as showed by a poorer classification performance. The results suggest that asymmetric neuronal degeneration in PD patients may contribute to the impairment of emotional communication.

show abstract

Section: Resultssupporting

confidence: 91%

Section: Preprocessingmentioning

confidence: 99%

Section: Detrended Fluctuation Analysis (Dfa)mentioning

confidence: 99%

Section: Support Vector Machine (Svm)mentioning

confidence: 99%

See 2 more Smart Citations

Hemispheric asymmetry non-linear analysis of EEG during emotional responses from idiopathic Parkinson’s disease patients

Yuvaraj¹,

Murugappan

2016

Cogn Neurodyn

Self Cite

View full text Add to dashboard Cite

show abstract

“…Research findings on the reason for this are inconclusive. Nine articles discussed the idea of using EEG-based emotion detection to provide assistance, monitoring, assessment, and diagnosis of Parkinson's disease in patients [47,106,107,114,[118][119][120][121][122].…”

Section: Othermentioning

confidence: 99%

Review and Classification of Emotion Recognition Based on EEG Brain-Computer Interface System Research: A Systematic Review

et al. 2017

View full text Add to dashboard Cite

Abstract:Recent developments and studies in brain-computer interface (BCI) technologies have facilitated emotion detection and classification. Many BCI studies have sought to investigate, detect, and recognize participants' emotional affective states. The applied domains for these studies are varied, and include such fields as communication, education, entertainment, and medicine. To understand trends in electroencephalography (EEG)-based emotion recognition system research and to provide practitioners and researchers with insights into and future directions for emotion recognition systems, this study set out to review published articles on emotion detection, recognition, and classification. The study also reviews current and future trends and discusses how these trends may impact researchers and practitioners alike. We reviewed 285 articles, of which 160 were refereed journal articles that were published since the inception of affective computing research. The articles were classified based on a scheme consisting of two categories: research orientation and domains/applications. Our results show considerable growth of EEG-based emotion detection journal publications. This growth reflects an increased research interest in EEG-based emotion detection as a salient and legitimate research area. Such factors as the proliferation of wireless EEG devices, advances in computational intelligence techniques, and machine learning spurred this growth.

show abstract

Different effects of levodopa and subthalamic stimulation on emotional conflict in Parkinson's disease

Martínez‐Fernández

Kibleur

Chabardès

et al. 2018

Human Brain Mapping

View full text Add to dashboard Cite

Parkinson's disease impairs the decoding of emotional stimuli reflecting alterations of the limbic cortico‐subcortical network. The objective of this study was to assess and compare the behavioral and electrophysiological effects of both levodopa and subthalamic stimulation on emotional processing in Parkinson's disease. Operated patients (n =16) and matched healthy subjects performed an emotional Stroop task, in which the emotion expressed by a face must be recognized while ignoring an emotional distractive word and that includes a neutral control sub‐task. Patients were tested in the four possible treatment conditions (off stim/off med; on stim/off med; off stim/on med; and on stim/on med). High‐resolution electroencephalography was recorded while performing the task. Patients made significantly more mistakes in facial emotion recognition than healthy subjects (p < .005). Untreated patients performed worse in the emotional trials than in the control sub‐task (p < .05). Fearful faces induced significantly slower reaction times than happy faces in patients (p = .0002), but not in the healthy subjects. The emotional Stroop effect with levodopa was significantly higher than with subthalamic stimulation when fearful faces were assessed (p = .0243). Conversely, treatments did not modulate the Stroop effect of the control sub‐task. EEG demonstrated that, compared with the untreated state, levodopa but not subthalamic stimulation significantly increases the amplitude of the event‐related potential N170 (p = .002 vs. p = .1, respectively), an electrophysiological biomarker of early aspects of facial processing. The activity of the N170 cortical sources within the right fusiform gyrus was increased by levodopa (p < .05) but not by stimulation. While levodopa normalizes the recognition of emotional facial expression and early EEG markers of emotional processing, subthalamic stimulation does not. Thus, operated patients require dopaminergic medication in addition to stimulation to treat emotional symptoms of Parkinson's disease.

show abstract

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

Cited by 60 publications

References 65 publications

Hemispheric asymmetry non-linear analysis of EEG during emotional responses from idiopathic Parkinson’s disease patients

Hemispheric asymmetry non-linear analysis of EEG during emotional responses from idiopathic Parkinson’s disease patients

Review and Classification of Emotion Recognition Based on EEG Brain-Computer Interface System Research: A Systematic Review

Different effects of levodopa and subthalamic stimulation on emotional conflict in Parkinson's disease

Contact Info

Product

Resources

About