A SimAM-Enhanced Multi-Resolution CNN with BiGRU for EEG Emotion Recognition: 4D-MRSimNet

This study proposes 4D-MRSimNet, a framework that employs attention mechanisms to focus on distinct dimensions. The approach applies enhancements to key responses in the spatial and spectral domains and provides a characterization of dynamic evolution in temporal domain, which extracts and integrates complementary emotional features to facilitate final classification. At the feature level, differential entropy (DE) and power spectral density (PSD) are combined within four core frequency bands (θ, α, β, and γ). These bands are recognized as closely related to emotional processing. This integration constructs a complementary feature representation that preserves both energy distribution and entropy variability. These features are organized into a 4D representation that integrates electrode topology, frequency characteristics, and temporal dependencies inherent in EEG signals. At the network level, a multi-resolution convolutional module embedded with SimAM attention extracts spatial and spectral features at different scales and adaptively emphasizes key information. A bidirectional GRU (BiGRU) integrated with temporal attention further emphasizes critical time segments and strengthens the modeling of temporal dependencies. Experiments show that our method achieves an accuracy of 97.68% for valence and 97.61% for arousal on the DEAP dataset and 99.60% for valence and 99.46% for arousal on the DREAMER dataset. The results demonstrate the effectiveness of complementary feature fusion, multidimensional feature representation, and the complementary dual attention enhancement strategy for EEG emotion recognition.