ArticleNeuroinformatics2022
Classification of Contrasting Discrete Emotional States Indicated by EEG Based Graph Theoretical Network Measures.
Article in Neuroinformatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers.
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Who cites it
35 citing papers in PubMed.
- Reliable resting-state EEG connectivity measures reveal brain network destabilization and cognitive decoupling following prolonged extreme working conditions.Cognitive neurodynamics · 2026Article
- Alpha oscillations during sunscreen application associated with willingness to pay.Experimental brain research · 2026Article
- Alterations in resting-state neural networks in subacute non-fluent aphasia after stroke: a fNIRS study.BMC neurology · 2026Article
- Emotion recognition based on the temporal patterns of electroencephalogram signals and electrodermal response signals using the TRANSFORMER network.Frontiers in neuroscience · 2026Article
- Classifying schizophrenia subtypes via resting-state EEG complexity networks.Scientific reports · 2025Article
- EEG-based functional connectivity patterns during boredom in an educational context.Scientific reports · 2025Article
- Deep convolutional neural network based archimedes optimization algorithm for heart disease prediction based on secured IoT enabled health care monitoring system.Scientific reports · 2025Article
- Music interventions and obstructive sleep apnea: a brain connectivity analysis.Biomedical engineering online · 2025Article
- Task-Independent Cognitive Workload Discrimination Based on EEG with Stacked Graph Attention Convolutional Networks.Sensors (Basel, Switzerland) · 2025Article
- EEG-based functional and effective connectivity patterns during emotional episodes using graph theoretical analysis.Scientific reports · 2025Article
- Heart rate and EEG gamma band connectivity in the ventral attention network during emotional movie stimulation in women with high emotion dysregulation.Frontiers in neuroscience · 2025Article
- High gamma EEG responses to emotional stimuli in virtual reality: insights from local activation and distributed characteristics.Frontiers in human neuroscience · 2025Article
- The brain selectively allocates energy to functional brain networks under cognitive control.Scientific reports · 2024Article
- Set-pMAE: spatial-spEctral-temporal based parallel masked autoEncoder for EEG emotion recognition.Cognitive neurodynamics · 2024Article
- SST-CRAM: spatial-spectral-temporal based convolutional recurrent neural network with lightweight attention mechanism for EEG emotion recognition.Cognitive neurodynamics · 2024Article
- STEADYNet: Spatiotemporal EEG analysis for dementia detection using convolutional neural network.Cognitive neurodynamics · 2024Article
- EEG emotion recognition based on an innovative information potential index.Cognitive neurodynamics · 2024Article
- PSPN: Pseudo-Siamese Pyramid Network for multimodal emotion analysis.Cognitive neurodynamics · 2024Article
- Graph theoretical brain connectivity measures to investigate neural correlates of music rhythms associated with fear and anger.Cognitive neurodynamics · 2024Article
- Synergistic integration of Multi-View Brain Networks and advanced machine learning techniques for auditory disorders diagnostics.Brain informatics · 2024Article
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2 authors.
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Abstract
The present study shows new findings that reveal the high association between emotional arousal and neuro-functional brain connectivity measures. For this purpose, contrasting discrete emotional states (happiness vs sadness, amusement vs disgust, calmness vs excitement, calmness vs anger, fear vs anger) are classified by using Support Vector Machines (SVMs) driven by Graph Theoretical segregation (clustering coefficients, transitivity, modularity) and integration (global efficiency, local efficiency) measures of the brain network. Emotional EEG data mediated by short duration video film clips is downloaded from publicly available database called DREAMER. Pearson Correlation (PC) and Spearman Correlation have been examined to estimate statistical dependencies between relatively shorter (6 sec) and longer (12 sec) non-overlapped EEG segments across the cortex. Then the corresponding brain connectivity encoded as a graph is transformed into binary numbers with respect to two different thresholds (60%max and mean). Statistical differences between contrasting emotions are obtained by using both one-way Anova tests and step-wise logistic regression modelling in accordance with variables (dependency estimation, segment length, threshold, network measure). Combined integration measures provided the highest classification accuracies (CAs) (75.00% 80.65%) when PC is applied to longer segments in accordance with particular threshold as the mean. The segregation measures also provided useful CAs (74.13% 80.00%), while the combination of both measures did not. The results reveal that discrete emotional states are characterized by balanced network measures even if both segregation and integration measures vary depending on arousal scores of audio-visual stimuli due to neurotransmitter release during video watching.
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