Phase-Locking Value Based Graph Convolutional Neural Networks for Emotion Recognition

Wang, Zhongmin; Tong, Yue; Xia, Heng

doi:10.1109/access.2019.2927768

Cited by 135 publications

(56 citation statements)

References 42 publications

(42 reference statements)

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“…Frontal EEG asymmetry refers to the difference in brain activity between the left and right frontal regions [12], [28]. This phenomenon is directly related to emotion and is used to recognize emotion [29]. Positive emotions are specifically associated with left hemisphere activity, whereas negative emotions are associated with more right hemispheric activity [30], [31].…”

Section: Introductionmentioning

confidence: 99%

Frontal EEG Asymmetry of Emotion for the Same Auditory Stimulus

2020

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Emotions play an important role in human interaction and decision-making processes. Frontal asymmetry in brain activity is a promising neurophysiological indicator of emotion. Emotions are psychologically explained by the valence-arousal model, but as yet, frontal asymmetry has not been fully explained by this model. In this study, we explored frontal asymmetry of emotions based on the valence-arousal model using the same auditory stimulus. Changes in emotional states using self-report questionnaires were investigated before and after the auditory stimulus. Spectral power and weighted phase lag index were calculated in the delta, theta, alpha, beta, and gamma bands. Phase-amplitude coupling was also measured to explore communication among different frequency bands associated with emotions. After the auditory stimulus, alpha power decreased in both left and right frontal regions and the deltaweighted phase lag index in the left-right regions was increased. However, no frontal asymmetry was identified after the auditory stimulus. Additionally, we explored the brain changes according to the valencearousal model based on emotional states. After the auditory stimulus, frontal asymmetry of alpha power was clearly observed only for negative valence. This finding was possible because subjective emotions were considered despite listening to the same stimulus. Finally, phase-amplitude coupling identified lefthemisphere dominance after the auditory stimulus, regardless of subjective emotions. These results may help us understand frontal asymmetry associated with emotional mechanisms. In addition, these findings can be used directly in the brain-computer interface to improve emotion recognition performance for realworld practical applications. INDEX TERMS Electroencephalogram (EEG), Emotion, Frontal asymmetry, Power spectral density (PSD), Weighted phase lag index (WPLI), Phase-amplitude coupling (PAC).

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Section: Introductionmentioning

confidence: 99%

Frontal EEG Asymmetry of Emotion for the Same Auditory Stimulus

2020

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“…Specifically, the coherence (COH), phase value index (PVI), and phase-locking value (PLV) capture pair-wise channel dependencies in the frequency domain. Moreover, the latter two gained attention due to their non-linear capability for unraveling latent connectivity patterns, which have proven valuable for applications, such as motor imagery and emotion recognition [14,15]. Nevertheless, the selection of the connectivity measure is not entirely straightforward.…”

Section: Introductionmentioning

confidence: 99%

A Discriminative Multi-Output Gaussian Processes Scheme for Brain Electrical Activity Analysis

et al. 2020

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The study of brain electrical activity (BEA) from different cognitive conditions has attracted a lot of interest in the last decade due to the high number of possible applications that could be generated from it. In this work, a discriminative framework for BEA via electroencephalography (EEG) is proposed based on multi-output Gaussian Processes (MOGPs) with a specialized spectral kernel. First, a signal segmentation stage is executed, and the channels from the EEG are used as the model outputs. Then, a novel covariance function within the MOGP known as the multispectral mixture kernel (MOSM) allows us to find and quantify the relationships between different channels. Several MOGPs are trained from different conditions grouped in bi-class problems, and the discrimination is performed based on the likelihood score of the test signals against all the models. Finally, the mean likelihood is computed to predict the correspondence of new inputs with each class’s existing models. Results show that this framework allows us to model the EEG signals adequately using generative models and allows analyzing the relationships between channels of the EEG for a particular condition. At the same time, the set of trained MOGPs is well suited to discriminate new input data.

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“…Phase locking value (PLV) was introduced by Lachaux et al [45] and there are many studies that present the effectiveness of PLV in EEG signal analysis, both for normal [46]- [49] and pathological [50]- [52] cases. Computing PLV between two signals, s x (t) and s y (t), requires instantaneous phase values of both signals.…”

Section: ) Phase Locking Valuesmentioning

confidence: 99%

A Channel Selection Method for Emotion Recognition From EEG Based on Swarm-Intelligence Algorithms

2021

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Increasing demand for human-computer interaction applications has escalated the need for automatic emotion recognition as emotions are essential for natural communication. There are various information sources that can be used for recognizing emotions, such as speech, facial expressions, body movements, and physiological signals. Among those physiological signals are more reliable for better affective communication with machines since they are almost impossible to control. Therefore, automatic emotion recognition from EEG signals has been a topic intensely investigated. Emotions are experiences that arise various cognitive functions observed in different frequency bands involving multiple brain areas and recognition from EEG with high accuracies is only possible with a large number of features extracted from the whole brain in various bands. Emotion regulation also requires integration of cognitive functions and thus functional connectivity between regions should also be considered. In this paper, we extract 736 features based on spectral power and phase-locking values. We particularly focus on finding salient features for emotion recognition using swarm-intelligence (SI) algorithms. We applied well-known classification algorithms for recognizing positive and negative emotions using the feature sets that are selected by these algorithms. Besides, features that are selected by all of them commonly are used as a new feature set. We report accuracies between 56.27% and 60.29% on the average; noting that by decreasing the feature size by 87.17% (from 736 to 94.40) an average accuracy of 60.01±8.93 was obtained with the random forest classifier. We also highlight the efficient electrode locations for emotion recognition. As a result, we define 11 channels as dominant and promising classification results are obtained.

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Phase-Locking Value Based Graph Convolutional Neural Networks for Emotion Recognition

Cited by 135 publications

References 42 publications

Frontal EEG Asymmetry of Emotion for the Same Auditory Stimulus

Frontal EEG Asymmetry of Emotion for the Same Auditory Stimulus

A Discriminative Multi-Output Gaussian Processes Scheme for Brain Electrical Activity Analysis

A Channel Selection Method for Emotion Recognition From EEG Based on Swarm-Intelligence Algorithms

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