Worry, generalized anxiety disorder, and emotion: Evidence from the EEG gamma band

Oathes, Desmond J.; Ray, William J.; Yamasaki, Alissa S.; Borkovec, Thomas D.; Castonguay, Louis G.; Newman, Michelle G.; Nitschke, Jack B.

doi:10.1016/j.biopsycho.2008.04.005

Cited by 135 publications

(101 citation statements)

References 23 publications

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“…Second, it is found that the classification performance of alpha, beta, and gamma bands is better than those of delta and theta bands in both the groups. This partly reflects that higher frequency bands play a more important role in emotion activities than lower frequency bands (Oathes and Ray, 2008;Wang et al, 2013) in PD patients and HC respectively. On the contrary, previous findings of EEG-emotion correlation in theta band has been reported (Aftanas and Golocheikine, 2001;Sammler et al, 2007).…”

Section: Choosing Of Svm Kernelsmentioning

confidence: 98%

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: Choosing Of Svm Kernelsmentioning

confidence: 98%

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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“…This result partly reflects that higher frequency bands play a more important role in emotion activities than lower frequency bands [63][64][65]. The current finding matches our previous study, where bispectrum emotion-specific features were mainly related to higher frequency band rather than lower frequency band in distinguishing six emotional states (happiness, sadness, fear, anger, surprise, and disgust) of PD patients and HC respectively with an averaged recognition rate of 70.10% ± 2.83% and 77.29% ± 1.73% [55].…”

Section: Resultsmentioning

confidence: 95%

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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“…This result shows that beta and gamma bands are the key bands in emotion recognition task. In other words, high frequency bands of EEG signals play a more important role in emotion activities than low frequency bands [22][23].…”

Section: Resultsmentioning

confidence: 99%

Channel Division Based Multiple Classifiers Fusion for Emotion Recognition Using EEG signals

Yan

Chen

2017

ITM Web Conf.

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Abstract.With the rapid development of computer technology, pervasive computing and wearable devices, EEG-based emotion recognition has gradually attracted much attention in affecting computing (AC) domain. In this paper, we propose an approach of emotion recognition using EEG signals based on the weighted fusion of multiple base classifiers. These base classifiers based on SVM are constructed using a channel division mechanism according to the neuropsychological theory that different brain areas are differ in processing intensity of emotional information. The outputs of channel base classifiers are integrated by a weighted fusion strategy which is based on the confidence estimation on each emotional label by each base classifier. The evaluation on the DEAP dataset shows that our proposed multiple classifiers fusion method outperforms individual channel base classifiers and the feature fusion method for EEG-based emotion recognition.

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Worry, generalized anxiety disorder, and emotion: Evidence from the EEG gamma band

Cited by 135 publications

References 23 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

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

Channel Division Based Multiple Classifiers Fusion for Emotion Recognition Using EEG signals

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