EEG-Based Emotion Recognition Using Regularized Graph Neural Networks
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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 RGNN model 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.
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