Emotion Processing by ERP Combined with Development and Plasticity

Ding, Rui; Li, Ping; Wang, Wei; Luo, Wenbo

doi:10.1155/2017/5282670

Cited by 31 publications

(30 citation statements)

References 190 publications

(206 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This proves that the brain was more active and more efficient in responding to the negative stimuli than to the positive ones. Other researchers have also reported that negative emotional activity required more attention, and the cortical activity was enhanced when the brain responded to negative stimuli (Ning, 2012;Jin et al, 2014;Ding et al, 2017).…”

Section: Relationships Between Bn Properties and Emotion Arousalmentioning

confidence: 96%

“…Previous event-related potential research have reported that significant differences of EEG signals among emotional states can be observed in P2 and P3 and slow-wave time windows (Ding et al, 2017;Wang and Li, 2017). However, the network connections and information processing and propagation in these time windows remain unclear.…”

Section: Superiority Of High Gamma Band Features In Emotion Recognitionmentioning

confidence: 96%

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.

111

View full text Add to dashboard Cite

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.

show abstract

Section: Relationships Between Bn Properties and Emotion Arousalmentioning

confidence: 96%

Section: Superiority Of High Gamma Band Features In Emotion Recognitionmentioning

confidence: 96%

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

Yang

Shu

et al. 2020

Front. Hum. Neurosci.

111

View full text Add to dashboard Cite

show abstract

“…Larger P1 amplitudes to angry vs. neutral faces were positively correlated with preschoolers' empathy scores. The P1 component has been shown to reflect attention allocation during early stage face processing (Ding et al, 2017). Preschoolers' ability to recruit greater attention resources during the processing of emotional faces, as indicated by enhanced P1 amplitudes, may be an important prerequisite to eventually empathize with others more effectively.…”

Section: Erps and Empathy-and Emotion-related Measuresmentioning

confidence: 99%

Preschoolers‘ sensitivity to negative and positive emotional facial expressions: An ERP study

Naumann¹,

Bayer²,

Dziobek³

2020

Preprint

View full text Add to dashboard Cite

This study aimed to expand the understanding of the neural-temporal trajectories ofemotion processing in preschoolers using electrophysiological measures. In particular, welooked at neural responses to the repetition of emotional faces. EEG was recorded whilechildren observed sequentially presented pairs of faces. In some trials, the pair of faces wasidentical, while in others they differed with regards to the emotional expression displayed(happy, fearful or neutral). We detected greater P1 and P3 amplitudes to angry compared toneutral facial expressions, but similar amplitudes for happy compared to neutral faces. Wedid not observe modulations of the N170 by emotional facial expressions. When investigatingpreschoolers’ sensitivity to the repetition of emotional facial expressions, we found no ERPamplitudes differences for repeated vs. new emotional facial expressions. Overall, the resultssupport the idea that basic mechanisms of emotion processing are developed in preschoolperiod. The trajectory of ERP components was similar to what has been reported foryounger and older age groups, suggesting consistency of order and relative timing of differentstages of emotion processing. Additionally, findings suggest that enhanced early neuralactivation for angry vs. neutral faces is related to increased empathic behavior. More work isneeded to determine whether the repetition of an emotion leads to more effective processingduring development.

show abstract

“…Emotion processing has been studied in detail in the last few decades. Electroencephalography (EEG) neuroimaging studies have revealed evoked potential responses (ERPs) that have been used extensively to understand emotion processing, with three main ERP stages proposed by Ding and colleagues (Ding et al, 2017); a first stage for automatic but coarse processing (N100 and P100), a second stage for distinguishing emotional and neutral facial expressions (N170 and VPP), and a third stage for distinguishing various emotional facial expressions (N300 and P300). There are studies both in favor (Hung et al, 2010) and against (Leppänen et al, 2007) these components, which have been replicated in different geographical locations .…”

Section: Introductionmentioning

confidence: 99%

Temporal Dynamics of Neural Processing of Facial Expressions and Emotions

Nara

Rathee

Molinaro

et al. 2021

Preprint

View full text Add to dashboard Cite

Emotion processing has been a focus of research in psychology and neuroscience for some decades. While the evoked neural markers in human brain activations in response to different emotions have been reported, the temporal dynamics of emotion processing has received less attention. Differences in processing speeds, that depend on emotion type, have not been determined. Furthermore, behavioral studies have found that the right side of the human face expresses emotions more accurately than the left side. Therefore, accounting for both the content of the emotion and the visual angle of presentation from the perspective of the viewer, here we have investigated variability in the discrimination of happy and sad faces when the visual angle of presentation was Positive (right side of the face) compared to Negative (left side of the face). Furthermore, the temporal dynamics involved in processing happy and sad emotions have been compared. Regardless of visual angle, happy emotions were processed faster than sad emotions. However, the evoked response to sad emotions significantly increased in amplitude compared to that for happy emotions, when faces were presented at Positive visual angles only. Source reconstruction from sensor-level ERFs show localized activities in ventral and dorsal stream, including fusiform gyrus, lingual gyrus, putamen and Pre and Post central gyrus. Multivariate pattern analysis (MVPA) confirmed these findings – demonstrating successful decoding of happy and sad emotions only occurred when the facial expression was viewed from a positive visual angle, and that happy emotions were processed faster than sad emotions.

show abstract

Emotion Processing by ERP Combined with Development and Plasticity

Cited by 31 publications

References 190 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

Preschoolers‘ sensitivity to negative and positive emotional facial expressions: An ERP study

Temporal Dynamics of Neural Processing of Facial Expressions and Emotions

Contact Info

Product

Resources

About