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Wanzhong Chen

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3 papers

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JBHI Journal 2026 Journal Article

EEG-Based Emotion Recognition Using Spatial-Temporal Graph-Aware Network with Channel Selection

Linlin Li
Wanzhong Chen

Electroencephalogram (EEG)-based emotion recognition holds great potential in intelligent human computer interaction and brain-computer interface systems, as the brain generates distinct electrical activity patterns under different emotional states. However, EEG information often contains data from numerous channels, leading to high computational cost and potential redundancy. Existing channel selection methods often rely on uniform rules, lacking frequency-specific adaptability and inter-channel modeling, which can cause information loss and reduced performance during dimensionality reduction. To address this issue, we propose a novel framework that combines discriminative channel selection with hierarchical spatial-temporal modeling to enhance both per formance and efficiency. In preprocessing, wavelet coherence and mutual information are used to adaptively select informative channels across multiple frequency bands. The selected signals are then processed by a Spatial Temporal Graph-aware Network (STG-Net), which models spatial relationships between channels through graph convolution, extracting spatial features from each time frame. Coupled with a temporal modeling module, the network further captures the evolving temporal patterns of emotional states across consecutive frames. Finally, frequency spatial-temporal features are fused for emotion classification. Compared to the state-of-the-art methods, our approach achieves superior performance in both recognition accuracy and model efficiency.

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JBHI Journal 2025 Journal Article

MMPI Net: A Novel Multimodal Model Considering the Similarities Between Perception and Imagination for Image Evoked EEG Decoding

Jinze Tong
Wanzhong Chen

In recent years, non-invasive electroencephalography (EEG) has been widely used to decode high-level cognitive functions, such as visual perception and imagination. The processes of visual perception and imagination in the human brain have been shown to share similar neural circuits and activation patterns in cognitive science. However, current research predominantly focuses on single cognitive processes, overlooking the natural commonalities between these processes and the insights that multimodal approaches can provide. To address this, this study proposes a novel multimodal model, MMPI Net, for jointly decoding EEG signals of visual image perception and imagination. MMPI Net comprises four components: Primitive Feature Extraction for Perception and Imagination (PFE), Cross-Semantic Feature Fusion (CSFF), Joint Semantic Feature Decoder (JSFD), and Semantic Classification (SC). To ensure the effectiveness of PFEM, an Improved Channel Attention Mechanism is introduced, which employs multiple parallel convolutional branches to enhance the extraction of important information and utilizes a Diverse Branch Block approach to reduce the parameter count. In the CSFF module, a cross-attention-based fusion method is designed to effectively capture and utilize intermodal information. In the JSFD phase, a Kolmogorov-Arnold Network is incorporated and coupled with linear layers to improve classification performance. Finally, a linear layer with Softmax is used as the SC module. Experimental results on two publicly available datasets show that, compared to models that use a single cognitive process, MMPI Net achieves average accuracy improvements of 14. 22% and 106. 1%, demonstrating its effectiveness

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JBHI Journal 2024 Journal Article

An Attention-Based Multi-Domain Bi-Hemisphere Discrepancy Feature Fusion Model for EEG Emotion Recognition

Linlin Gong
Wanzhong Chen
Dingguo Zhang

Electroencephalogram (EEG)-based emotion recognition has become a research hotspot in the field of brain-computer interface. Previous emotion recognition methods have overlooked the fusion of multi-domain emotion-specific information to improve performance, and faced the challenge of insufficient interpretability. In this paper, we proposed a novel EEG emotion recognition model that combined the asymmetry of the brain hemisphere, and the spatial, spectral, and temporal multi-domain properties of EEG signals, aiming to improve emotion recognition performance. Based on the 10–20 standard system, a global spatial projection matrix (GSPM) and a bi-hemisphere discrepancy projection matrix (BDPM) are constructed. A dual-stream spatial-spectral-temporal convolution neural network is designed to extract depth features from the two matrix paradigms. Finally, the transformer-based fusion module is used to learn the dependence of fused features, and to retain the discriminative information. We conducted extensive experiments on the SEED, SEED-IV, and DEAP public datasets, achieving excellent average results of 98. 33/2. 46 $\%$, 92. 15/5. 13 $\%$, 97. 60/1. 68 $\%$ (valence), and 97. 48/1. 42 $\%$ (arousal) respectively. Visualization analysis supports the interpretability of the model, and ablation experiments validate the effectiveness of multi-domain and bi-hemisphere discrepancy information fusion.

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