  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.
