In this paper, we propose a regularized graph neural network (RGNN) for EEG-based emotion recognition. EEG signals measure the neuronal activities on different brain regions via electrodes attached on them. Existing studies do not exploit the topological structure of EEG signals effectively. Our RGNNmodel is biologically supported and captures both local and global inter-channel relations.  In addition, we propose two regularizers, namely NodeDAT and EmotionDL , to improve the robustness of our model against cross-subject EEG variations and noisy labels during recording . To thoroughly evaluate our model, we conduct extensive experiment in both subject-dependent and subject-independent classification settings on two public datasets  SEED and SEED-IV. Our model obtains better performance than a few competitive baselines such as SVM, DBN, DGCNN, BiDANN, and the state-of-the-art BiHDM on most of the tasks . Our model analysis demonstrates that our proposed biologically-supported adjacency matrix and two regularizers contribute consistent and significant gain to the performance of our model . Investigations on the neuronal activities reveal that pre-frontal, parietal and occipital regions may be the most informative regions in emotion recognition . In addition,  local inter-channel relations between (FP1, AF3), (F6, F8) and (FP2, AF4) may  provide useful information as well .
 EEG signals measure the neuronal activities on different brain regions via electrodes . Many existing studies on EEG-based emotion recognition do not exploit the topological structure of EEG signals . In this paper, we propose a regularized graph neural network (RGNN) for EEG-based emotion recognition, which is biologically supported and captures both local and global inter-channel relations. Specifically, we model the inter-channel relations in EEG signals via an adjacency matrix in our graph neural network where the connection and sparseness of the adjacency matrix are supported by the neurosicience theories of human URLanization. In addition, we propose two regularizers, namely node-wise domain adversarial training (NodeDAT) and emotion-aware distribution learning (EmotionDL) , to improve the robustness of our model against cross-subject EEG variations and noisy labels , respectively . To thoroughly evaluate our model, we conduct extensive experiments in both subject-dependent and subject-independent classification settings on two public datasets : SEED and SEED-IV. Our model obtains better performance than competitive baselines such as SVM, DBN, DGCNN, BiDANN, and the state-of-the-art BiHDM in most experimental settings . Our model analysis demonstrates that the proposed biologically supported adjacency matrix and two regularizers contribute consistent and significant gain to the performance . Investigations on the neuronal activities reveal that pre-frontal, parietal and occipital regions may be the most informative regions for emotion recognition, which is consistent with relevant prior studies . In addition, experimental results suggest that global inter-channel relations between the left and right hemispheres are important for emotion recognition and local inter-channel relations between (FP1, AF3), (F6, F8) and (FP2, AF4) may also provide useful information .
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In this paper, we propose a regularized graph neural network (RGNN) for EEG-based emotion recognition.
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attached on them. Existing studies
. Many existing studies on EEG-based emotion recognition
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effectively. Our RGNNmodel
. In this paper, we propose a regularized graph neural network (RGNN) for EEG-based emotion recognition, which
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Specifically, we model the inter-channel relations in EEG signals via an adjacency matrix in our graph neural network where the connection and sparseness of the adjacency matrix are supported by the neurosicience theories of human URLanization.
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NodeDAT and EmotionDL
node-wise domain adversarial training (NodeDAT) and emotion-aware distribution learning (EmotionDL)
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during recording
, respectively
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experiment
experiments
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:
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a few
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on most of the tasks
in most experimental settings
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our proposed biologically-supported
the proposed biologically supported
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of our model
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in emotion recognition
for emotion recognition, which is consistent with relevant prior studies
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experimental results suggest that global inter-channel relations between the left and right hemispheres are important for emotion recognition and
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also
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as well