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 fusion method for EEG emotion recognition. First, the original GC matrices between the EEG signals at each frequency band are calculated via GC analysis, and then they are adaptively converted to asymmetric binary GC matrices through an optimal threshold. Then, a kind of novel GC-based GCN feature (GC-GCN) is constructed by using differential entropy features and the binary GC matrices as the node values and adjacency matrices, respectively. Finally, on the basis of the GC-GCN features, a new multi-frequency band feature fusion method (GC-F-GCN) is proposed, which integrates the graph information of the EEG signals at different frequency bands for the same node. The experimental results demonstrate that the proposed GC-F-GCN method achieves better recognition performance than the state-of-the-art GCN methods, for which average accuracies of 97.91%, 98.46%, and 98.15% were achieved for the arousal, valence, and arousal–valence classifications, respectively.

TSANN-TG: Temporal–Spatial Attention Neural Networks with Task-Specific Graph for EEG Emotion Recognition

“…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].…”

FCAN–XGBoost: A Novel Hybrid Model for EEG Emotion Recognition

“…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.…”

“…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.…”

“…They can reflect human mental state and emotional changes well [ 27 ]. More and more researchers are using EEG signals for emotion recognition research and have achieved better results than non-physiological signals such as facial expressions, speech intonation, and body movements [ 28 , 29 , 30 , 31 , 32 ]. While it is true that previous research on emotion recognition using EEG has yielded impressive results, there are still some urgent problems that need to be addressed, such as low recognition accuracy and high computational cost [ 26 ].…”