ArticleFrontiers in human neuroscience2020
High Gamma Band EEG Closely Related to Emotion: Evidence From Functional Network.
Article in Frontiers in human neuroscience, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 55 papers.
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55 citing papers in PubMed, 171 citations in OpenAlex.
- Electroencephalography-Based Emotion Recognition Using Auditory Stimulation for Affective Brain-Computer Interfaces.Sensors (Basel, Switzerland) · 2026Article
- EEG based multifunctional connectivity fusion across frequency bands and parameters promote motor function assessment in stroke: a pilot study.Journal of neuroengineering and rehabilitation · 2026Article
- A multi-task EEG emotion recognition method based on emotion-dimension coupling constraints.Scientific reports · 2026Article
- Transformer-based emotion recognition in interactive art: A multimodal neural approach.PloS one · 2026Article
- Acoustic features of music differentially modulate anxiety through EEG gamma oscillations and prefrontal connectivity.Scientific reports · 2025Article
- Graph-theoretical analysis of EEG-based functional connectivity during emotional experience in virtual reality for emotion recognition.Scientific reports · 2025Article
- The Development of Lateralized Brain Oscillations in Infants: Lessons From Autism.Developmental psychobiology · 2025Article
- EEG-based functional connectivity patterns during boredom in an educational context.Scientific reports · 2025Article
- An automated extraction of spectral-temporal and spatial-temporal features of EEG for emotion detection.Brain informatics · 2025Article
- A Pilot Study on Emotional Equivalence Between VR and Real Spaces Using EEG and Heart Rate Variability.Sensors (Basel, Switzerland) · 2025Article
- Modeling Emotional Arousal With Turbulence Measured by EEG.Psychophysiology · 2025Article
- EEG reveals the cognitive impact of polarized content in short video scenarios.Scientific reports · 2025Article
- Neural correlates of power-related postures and their behavioural consequences: a preliminary electrophysiological investigation.Social cognitive and affective neuroscience · 2025Article
- Improved EEG-Based Emotion Classification via Stockwell Entropy and CSP Integration.Entropy (Basel, Switzerland) · 2025Article
- Single-Trial Electroencephalography Discrimination of Real, Regulated, Isometric Wrist Extension and Wrist Flexion.Biomimetics (Basel, Switzerland) · 2025Article
- High gamma EEG responses to emotional stimuli in virtual reality: insights from local activation and distributed characteristics.Frontiers in human neuroscience · 2025Article
- Brain-computer-interface-driven artistic expression: real-time cognitive visualization in the pangolin scales animatronic dress and screen dress.Frontiers in human neuroscience · 2025Article
- Harmonizing the past: EEG-based brain network unveil modality-specific mechanisms of nostalgia.Frontiers in psychology · 2025Article
- An Ensemble Deep Learning Approach for EEG-Based Emotion Recognition Using Multi-Class CSP.Biomimetics (Basel, Switzerland) · 2024Article
- Partial prior transfer learning based on self-attention CNN for EEG decoding in stroke patients.Scientific reports · 2024Article
Corrections and comments
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Authors and funding
6 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.