## 4 Experiments
### Experimental Results  
1. Model Comparison  
The experimental results are shown in Table 1. The results demonstrated that our proposed SGAAE-AC method performed exceptionally well on CHBMP datasets, especially in terms of ACC, NMI, ARI and F-score. Methods solely relying on a single information source displayed ACC values between 0.311 (GraphEncoder) and 0.524 (K-means) on the CHBMP dataset. Our SGAAE-AC method, integrating structural and content information, achieved a remarkable ACC of 0.754, significantly surpassing these methods. Among strategies incorporating graph structure and attributes, performance improved progressively from GAE to AGC, with AGC reaching 0.681 and ARVGE 0.621 in ACC. Yet, SGAAE-AC stands out even among these, delivering superior results across various metrics through optimized information use and strategic enhancements. The SGAAE-AC model outperformed the leading AGC method by 0.073 in ACC, thanks to its innovative use of label feedback from clustering to refine graph embeddings, reducing misclassifications and boosting ACC. Improvements in NMI and ARI by 0.055 and 0.021, respectively, over AGC, are credited to SGAAE-AC's simultaneous optimization of graph reconstruction and clustering loss, enhancing feature representation's relevance and the model's self-learning capabilities. Moreover, the F-score benefits from precise data portrayal and efficient classification, further elevated by the Laplacian filter's role in enhancing local and global information coherence, thus improving the model's ability to discern EEG patterns by age, which is reflected in a higher F-score. 2. Experimental Parameter Adjustments  
Our research adjusted the hidden layer dimensions of the graph attention autoencoder from 16 to 512 neurons and the number of attention heads from 1 to 8. We can see from Figure 4a,b a stable improvement in clustering performance when the dimension of the hidden layer increased from 128 to 256 neurons. However, further increases in the number of embedding layer neurons showed some performance fluctuations. Notably, despite fluctuations, the overall ACC and NMI scores remained at a high level. As for the number of attention heads, a stable improvement in clustering performance was observed when increasing from 1 to 2 heads. However, further increases led to a downward trend in performance. These observations suggest that selecting appropriate dimensions for the embedding layer and the number of attention heads is crucial for optimizing model performance, especially with complex graph structure data. Excessive neurons or attention heads might lead to performance degradation, likely due to overfitting or increased model complexity. Hence, our research emphasizes the importance of finding an optimal parameter balance in model design. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Method** & **Dataset** & **ACC** & **NMI** & **ARI** & **F-Score** \\ \hline K-means & CHBMP & 0.524 & 0.302 & 0.261 & 0.415 \\ Spectral & CHBMP & 0.374 & 0.245 & 0.208 & 0.324 \\ GraphEncoder & CHBMP & 0.311 & 0.103 & 0.163 & 0.208 \\ DNGR & CHBMP & 0.432 & 0.188 & 0.176 & 0.231 \\ DeepWalk & CHBMP & 0.453 & 0.311 & 0.226 & 0.291 \\ GAE & CHBMP & 0.591 & 0.371 & 0.347 & 0.408 \\ VGAE & CHBMP & 0.608 & 0.347 & 0.398 & 0.402 \\ ARVGE & CHBMP & 0.621 & 0.397 & 0.328 & 0.426 \\ AGC & CHBMP & 0.681 & 0.424 & 0.402 & 0.503 \\ SGAAE-AC & CHBMP & 0.754 & 0.479 & 0.423 & 0.663 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: EEG age clustering experiment results on the CHBMP dataset. Ablation experiment  
To ensure the rigor of our experimental results, we conducted ablation studies to explore the impact of the optimization module on our model's parameters. Table 2 shows the results of this experiment, and Figure 5 uses t-SNE dimensionality reduction [37] to display changes in learned embeddings for the EEG age-clustering task within the CHBMP dataset across different training configurations. Original Signal Scatter: Figure 5 depicts the distribution under original conditions.