Brain Effective Connectivity Analysis from EEG for Positive and Negative Emotion

Zhang, Jianhai; Zhao, Shaokai; Huang, Wenhao; Hu, Sanqing

doi:10.1007/978-3-319-70093-9_90

Cited by 18 publications

(12 citation statements)

References 14 publications

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“…The average CC, Le, and Ge values in the high gamma band for the positive and the negative stimuli were higher than those for the neutral stimuli, whereas the average CPL value for the positive and the negative stimuli was smaller than that for the neutral stimuli. These results further demonstrate that the BNs of the positive and the negative stimuli were more efficient than those for the neutral stimulus (Zhang et al, 2017). Furthermore, the CC, Le, and Ge values for the negative pictures were higher (p < 0.01) than those for the positive picture; the negative pictures had a smaller (p < 0.01) CPL value than the positive pictures.…”

Section: Relationships Between Bn Properties and Emotion Arousalsupporting

confidence: 61%

“…These results further demonstrate that the high gamma components in EEG are associated with emotion processing. Differences exist in the DE distribution in multiple brain regions, which also demonstrates that emotional activity requires the cooperation of multiple related brain regions (Bassett and Bullmore, 2006;Zhang et al, 2017).…”

Section: Neural Signature Of Emotional States In the High Gamma Bandmentioning

confidence: 92%

“…Hossein et al proposed that exposure to joyful stimuli elicits stronger connectivity in the frontal inter/intra-hemispheric regions than the connectivity elicited via exposure to neutral or melancholic stimuli (Hossein and Sahar, 2016). Zhang et al found that the prefrontal region plays the most important role in emotion processing and interacts with almost all other regions (Zhang et al, 2017). Furthermore, Li et al reported that connections with significant differences between the negative and neutral valences in the gamma band (30-48 Hz) are much denser than the connections in the beta band (12-30 Hz).…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

High Gamma Band EEG Closely Related to Emotion: Evidence From Functional Network

Yang

Shu

et al. 2020

Front. Hum. Neurosci.

110

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High-frequency electroencephalography (EEG) signals play an important role in research on human emotions. However, the different network patterns under different emotional states in the high gamma band (50-80 Hz) remain unclear. In this paper, we investigate different emotional states using functional network analysis on various frequency bands. We constructed multiple functional networks on different frequency bands and performed functional network analysis and time-frequency analysis on these frequency bands to determine the significant features that represent different emotional states. Furthermore, we verified the effectiveness of these features by using them in emotion recognition. Our experimental results revealed that the network connections in the high gamma band with significant differences among the positive, neutral, and negative emotional states were much denser than the network connections in the other frequency bands. The connections mainly occurred in the left prefrontal, left temporal, parietal, and occipital regions. Moreover, long-distance connections with significant differences among the emotional states were observed in the high frequency bands, particularly in the high gamma band. Additionally, high gamma band fusion features derived from the global efficiency, network connections, and differential entropies achieved the highest classification accuracies for both our dataset and the public dataset. These results are consistent with literature and provide further evidence that high gamma band EEG signals are more sensitive and effective than the EEG signals in other frequency bands in studying human affective perception.

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Section: Relationships Between Bn Properties and Emotion Arousalsupporting

confidence: 61%

Section: Neural Signature Of Emotional States In the High Gamma Bandmentioning

confidence: 92%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

High Gamma Band EEG Closely Related to Emotion: Evidence From Functional Network

Yang

Shu

et al. 2020

Front. Hum. Neurosci.

110

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“…Alipour et al [27] found that alpha connectivity of the fronto-central connections was primarily associated with emotional music, especially in valence. In Zhang et al [41], significant differences in effective connectivity were found between positive and negative emotions. Specifically, frontal regions and connectivity with other regions play a critical role in emotion processing.…”

Section: A Emotion In Response To Auditory Stimulimentioning

confidence: 95%

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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“…In order to evaluate the performance of the proposed QTFD-based features in recognizing different emotion classes, we have developed three performance evaluation analyses: Channel-based analysis: In this analysis, we investigate the effect of utilizing various groups of EEG channels, which cover different regions of the brain, on the accuracy of recognizing the emotion classes defined based on the four emotion labeling schemes. Recently, several studies have indicated that the frontal, prefrontal, temporal, parietal and occipital regions of the brain are involved in emotional responses [ 25 , 32 , 51 , 52 , 53 , 54 , 55 , 56 , 57 , 58 ]. In particular, Mohammadi et al [ 58 ] utilized five pairs of electrodes that cover the frontal and frontal parietal regions of the brain, where these pairs are F3-F4, F7-F8, FC1-FC2, FC5-FC6 and FP1-FP2, to recognize emotional states defined based on the 2D arousal-valence plane.…”

Section: Methodsmentioning

confidence: 99%

EEG-Based Emotion Recognition Using Quadratic Time-Frequency Distribution

Alazrai

Homoud

Alwanni

et al. 2018

Sensors

103

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Accurate recognition and understating of human emotions is an essential skill that can improve the collaboration between humans and machines. In this vein, electroencephalogram (EEG)-based emotion recognition is considered an active research field with challenging issues regarding the analyses of the nonstationary EEG signals and the extraction of salient features that can be used to achieve accurate emotion recognition. In this paper, an EEG-based emotion recognition approach with a novel time-frequency feature extraction technique is presented. In particular, a quadratic time-frequency distribution (QTFD) is employed to construct a high resolution time-frequency representation of the EEG signals and capture the spectral variations of the EEG signals over time. To reduce the dimensionality of the constructed QTFD-based representation, a set of 13 time- and frequency-domain features is extended to the joint time-frequency-domain and employed to quantify the QTFD-based time-frequency representation of the EEG signals. Moreover, to describe different emotion classes, we have utilized the 2D arousal-valence plane to develop four emotion labeling schemes of the EEG signals, such that each emotion labeling scheme defines a set of emotion classes. The extracted time-frequency features are used to construct a set of subject-specific support vector machine classifiers to classify the EEG signals of each subject into the different emotion classes that are defined using each of the four emotion labeling schemes. The performance of the proposed approach is evaluated using a publicly available EEG dataset, namely the DEAPdataset. Moreover, we design three performance evaluation analyses, namely the channel-based analysis, feature-based analysis and neutral class exclusion analysis, to quantify the effects of utilizing different groups of EEG channels that cover various regions in the brain, reducing the dimensionality of the extracted time-frequency features and excluding the EEG signals that correspond to the neutral class, on the capability of the proposed approach to discriminate between different emotion classes. The results reported in the current study demonstrate the efficacy of the proposed QTFD-based approach in recognizing different emotion classes. In particular, the average classification accuracies obtained in differentiating between the various emotion classes defined using each of the four emotion labeling schemes are within the range of 73.8%–86.2%. Moreover, the emotion classification accuracies achieved by our proposed approach are higher than the results reported in several existing state-of-the-art EEG-based emotion recognition studies.

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Brain Effective Connectivity Analysis from EEG for Positive and Negative Emotion

Cited by 18 publications

References 14 publications

High Gamma Band EEG Closely Related to Emotion: Evidence From Functional Network

High Gamma Band EEG Closely Related to Emotion: Evidence From Functional Network

Frontal EEG Asymmetry of Emotion for the Same Auditory Stimulus

EEG-Based Emotion Recognition Using Quadratic Time-Frequency Distribution

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