mlsanityData to 14 Oct 2026

Graph Convolutional Network with Connectivity Uncertainty for EEG-based Emotion Recognition

arXiv 2310.14165 ↗First posted 22 Oct 2023cs.LG cs.AI eess.SPcited by 3

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Abstract (arXiv)

Automatic emotion recognition based on multichannel Electroencephalography (EEG) holds great potential in advancing human-computer interaction. However, several significant challenges persist in existing research on algorithmic emotion recognition. These challenges include the need for a robust model to effectively learn discriminative node attributes over long paths, the exploration of ambiguous topological information in EEG channels and effective frequency bands, and the mapping between intrinsic data qualities and provided labels. To address these challenges, this study introduces the distribution-based uncertainty method to represent spatial dependencies and temporal-spectral relativeness in EEG signals based on Graph Convolutional Network (GCN) architecture that adaptively assigns weights to functional aggregate node features, enabling effective long-path capturing while mitigating over-smoothing phenomena. Moreover, the graph mixup technique is employed to enhance latent connected edges and mitigate noisy label issues. Furthermore, we integrate the uncertainty learning method with deep GCN weights in a one-way learning fashion, termed Connectivity Uncertainty GCN (CU-GCN). We evaluate our approach on two widely used datasets, namely SEED and SEEDIV, for emotion recognition tasks. The experimental results demonstrate the superiority of our methodology over previous methods, yielding positive and significant improvements. Ablation studies confirm the substantial contributions of each component to the overall performance.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 27 of its 68 references that are in the map. First posted on arXiv 22 Oct 2023.

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27 of 68 references.

References · 27

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15 more
  1. 2015Variational Dropout and the Local Reparameterization Trickcited by 490
  2. 2016Learning Convolutional Neural Networks for Graphscited by 314
  3. 2019Graph Wavelet Neural Networkcited by 131
  4. 2020Revisiting Graph based Collaborative Filtering: A Linear Residual Graph Convolutional Network Approachcited by 120
  5. 2019EEG-Based Emotion Recognition Using Regularized Graph Neural Networkscited by 97
  6. 2018Bayesian graph convolutional neural networks for semi-supervised classificationcited by 89
  7. 2020Bayesian Graph Neural Networks with Adaptive Connection Samplingcited by 81
  8. 2021Self-Supervised Graph Neural Networks for Improved Electroencephalographic Seizure Analysiscited by 76
  9. 2021How Framelets Enhance Graph Neural Networkscited by 46
  10. 2019A Novel Bi-hemispheric Discrepancy Model for EEG Emotion Recognitioncited by 42
  11. 2017Spatial-Temporal Recurrent Neural Network for Emotion Recognitioncited by 38
  12. 2019Optimizing Generalized PageRank Methods for Seed-Expansion Community Detectioncited by 35
  13. 2021EEG-GNN: Graph Neural Networks for Classification of Electroencephalogram (EEG) Signalscited by 28
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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 1; 2025: 1; 2026: 1.

3 papers.

  1. 2024Shared Attention-based Autoencoder with Hierarchical Fusion-based Graph Convolution Network for sEEG SOZ Identificationcited by 0
  2. 2025Fusion of Multiscale Features Via Centralized Sparse-attention Network for EEG Decodingcited by 0
  3. 2026NeuroDETR: Multiscale Discrete Spatiotemporal Tokenization for Asynchronous EEG Event Detectioncited by 0