Analysis of functional brain connections for positive–negative emotions using phase locking value

Daşdemir, Yaşar; Yıldırım, Esen; Yıldırım, Serdar

doi:10.1007/s11571-017-9447-z

Cited by 72 publications

(42 citation statements)

References 84 publications

(101 reference statements)

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“…This will provide a sufficient theoretical basis for future studies on perceptual cognition and the construction of global neural models for brain function, which is a new brain theory of neuro combined with holism and reductionism. Its scientific significance lies in the subthreshold neural energy is directly associated with dark energy of neurons in the default model of network [32,33], while 90% of the brain energy (dark energy of the neural) as well as the inherent neural energy metabolism of the spontaneous activity when the brain is at resting state has not been deeply studied [26,34]. Owing to that the nature of neural energy metabolism is intrinsic and inherent in case of the spontaneous activity, the contribution to the cognitive neuroscience is what we have not known.…”

Section: Resultsmentioning

confidence: 99%

The essence of neuronal activity from the consistency of two different neuron models

Wang

Zhu

2018

Nonlinear Dyn

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Based on the H-H equation, this study has proposed the calculation and analysis of energy expenditure for a single neuron which is activated at supthreshold and subthreshold, as well as the criterion of the energy expenditure of neurons activated supthreshold and subthreshold, which was the maximum power of a sodium ion pump. Results of the study showed that not only the electrophysiological activities of neurons were strictly restricted by the energy levels in the brain, but also the activities of neurons also had dual nature, meaning that subthreshold neurons were mainly with energy expenditure, while sup-threshold neurons were with both energy absorption and energy expenditure. These new findings were compared with the novel neuro-biophysical models that we have published last year, uncovering that the two models were essentially equivalent.

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Section: Resultsmentioning

confidence: 99%

The essence of neuronal activity from the consistency of two different neuron models

Wang

Zhu

2018

Nonlinear Dyn

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“…Positive emotions are specifically associated with left hemisphere activity, whereas negative emotions are associated with more right hemispheric activity [30], [31]. This frontal asymmetry between the left and right frontal regions is also observed in relation to brain connectivity [25]. In some cases, however, there is no change in frontal asymmetry after the auditory stimulus [32].…”

Section: Introductionmentioning

confidence: 92%

“…In recent years, some studies have investigated frontal asymmetry, noting the relationships between regions using brain connectivity and associations between frequencies using cross-frequency coupling to reflect neural communications [25], [46]. Greethanjali et al [47] showed asymmetrical alpha connectivity in frontal regions according to personal preferences.…”

Section: B Frontal Eeg Asymmetry Of Emotionmentioning

confidence: 99%

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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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“…Brain connectivity can inform us about how different brain areas communicate [18] and are encapsulating complementary information compared to brain activity [19]. Brain subnetwork over frontal brain areas plays a control role for emotions [20]. Increased delta activity (synchronization) has been reported in both pleasant and unpleasant stimuli [21] while increased delta connectivity has been found after listening to music [22].…”

Section: Introductionmentioning

confidence: 99%

A Multiplex Connectivity Map of Valence-Arousal Emotional Model

2020

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A high number of studies have already demonstrated an electroencephalography (EEG)-based emotion recognition system with moderate results. Emotions are classified into discrete and dimensional models. We focused on the latter that incorporates valence and arousal dimensions. The mainstream methodology is the extraction of univariate measures derived from EEG activity from various frequencies classifying trials into low/high valence and arousal levels. Here, we evaluated brain connectivity within and between brain frequencies under the multiplexity framework. We analyzed an EEG database called DEAP that contains EEG responses to video stimuli and users' emotional self-assessments. We adopted a dynamic functional connectivity analysis under the notion of our dominant coupling model (DoCM). DoCM detects the dominant coupling mode per pair of EEG sensors, which can be either within frequencies coupling (intra) or between frequencies coupling (cross-frequency). DoCM revealed an integrated dynamic functional connectivity graph (IDFCG) that keeps both the strength and the preferred dominant coupling mode. We aimed to create a connectomic mapping of valence-arousal map via employing features derive from IDFCG. Our results outperformed previous findings succeeding to predict in a high accuracy participants' ratings in valence and arousal dimensions based on a flexibility index of dominant coupling modes.

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Analysis of functional brain connections for positive–negative emotions using phase locking value

Cited by 72 publications

References 84 publications

The essence of neuronal activity from the consistency of two different neuron models

The essence of neuronal activity from the consistency of two different neuron models

Frontal EEG Asymmetry of Emotion for the Same Auditory Stimulus

A Multiplex Connectivity Map of Valence-Arousal Emotional Model

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