2022
DOI: 10.3390/brainsci12121649
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Granger-Causality-Based Multi-Frequency Band EEG Graph Feature Extraction and Fusion for Emotion Recognition

Abstract: Graph convolutional neural networks (GCN) have attracted much attention in the task of electroencephalogram (EEG) emotion recognition. However, most features of current GCNs do not take full advantage of the causal connection between the EEG signals in different frequency bands during the process of constructing the adjacency matrix. Based on the causal connectivity between the EEG channels obtained by Granger causality (GC) analysis, this paper proposes a multi-frequency band EEG graph feature extraction and … Show more

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Cited by 7 publications
(4 citation statements)
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“…Zhang et al proposed a fusion strategy grounded in graph theory, which integrates features from multiple frequency bands into a unified representation for emotion recognition. This fusion approach enables the amalgamation of complementary information across different frequency bands, resulting in enhanced performance in emotion classification [34]. Tian et al, on the other hand, utilized various functional connectivity features derived from EEG data, such as coherence and phase synchronization, to augment the discriminative power of the graph convolutional neural network (GCN) model [35].…”
Section: Graph Learning and Gnns For Eeg Emotion Recognitionmentioning
confidence: 99%
“…Zhang et al proposed a fusion strategy grounded in graph theory, which integrates features from multiple frequency bands into a unified representation for emotion recognition. This fusion approach enables the amalgamation of complementary information across different frequency bands, resulting in enhanced performance in emotion classification [34]. Tian et al, on the other hand, utilized various functional connectivity features derived from EEG data, such as coherence and phase synchronization, to augment the discriminative power of the graph convolutional neural network (GCN) model [35].…”
Section: Graph Learning and Gnns For Eeg Emotion Recognitionmentioning
confidence: 99%
“…The experimental results showed that the fusion of two features was superior to a single feature. Zhang et al [ 31 ] proposed a multi-band feature fusion method GC–F-GCN based on Granger causality (GC) and graph convolutional neural network (GCN) for emotional recognition of EEG signals. The GC–F-GCN method demonstrated superior recognition performance than the state-of-the-art GCN method in the binary classification task, achieving average accuracies of 97.91%, 98.46%, and 98.15% for arousal, valence, and arousal–valence classification, respectively.…”
Section: Related Workmentioning
confidence: 99%
“…In this study, we decomposed the EEG signals into four frequency bands: theta (4-8 Hz), alpha (8-14 Hz), beta (14-31 Hz), and gamma (31)(32)(33)(34)(35)(36)(37)(38)(39)(40)(41)(42)(43)(44)(45). Theta waves have a frequency range of 4-8 Hz and are present in the brain's frontal lobe when individuals experience mental relaxation or light sleep.…”
Section: Data Processingmentioning
confidence: 99%
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