## 3 Main Framework, Dataset, and Feature Extraction Overview
### Feature Extraction  
In this process, extracting features from EEG signals is crucial, as they carry different information, depending on whether the subject is healthy or epileptic and whether they are in a seizure or non-seizure state. Initially, EEG time series are transformed into associated networks using various methods. The adjacency matrix of each network is stored, and the average degree, average clustering coefficient, density, network diameter, global efficiency, and average path length are calculated for networks under five different algorithms: the VG, the HVG, the LPVG, MFDM, and the QG [19; 20; 21]. Additionally, the average jump length \(\Delta\) is computed for networks under the QG algorithm [13]. The network measures obtained from each algorithm are then standardized. Subsequently, an analysis of variance (ANOVA) is used to filter out columns with significance levels greater than 0.05. In statistics, 0.05 is  
Figure 8: Typical EEG signals in each of the five sets; from top to bottom: (**A**) (healthy, eyes open), (**B**) (healthy, eyes closed), (**C**) (epileptic, opposite zone), (**D**) (epileptic, epileptogenic zone), and (**E**) (epileptic, seizure). often used as a standard threshold to determine whether a result is statistically significant. Choosing 0.05 as the significance level means that, if the \(p\)-value of a feature is less than 0.05, it is considered that the feature has a significant impact on the classification task. On the contrary, it is considered that the influence is not significant. This selection was based on balancing considerations in statistical conventions and practical applications, aiming to include as many features useful for classification as possible while controlling for false positive rates. The strength of ANOVA is its simplicity and intuitiveness, which directly assesses the relationship between features and labels and is effective in many cases. As shown in Algorithm 1, the ANOVA feature concatenation method selects significant features based on their \(p\)-values. This method is effective in reducing the dimensionality of the dataset by retaining only those features that have a statistically significant impact on the target variable. ```
1:Input: Dataset \(X\in\mathbb{R}^{m\times n}\), labels \(Y\in\mathbb{R}^{m}\), significance threshold \(\alpha=0.05\)
2:Output: Concatenated feature set \(X_{\text{selected}}\)
3:Initialize empty list selected_features
4:for each feature \(f_{i}\) in \(X\)do
5: Perform ANOVA to compute p-value \(p_{i}\) for feature \(f_{i}\)
6:if\(p_{i}<\alpha\)then
7: Append feature \(f_{i}\) to selected_features
8:endif
9:endfor
10: Concatenate the selected features into a new set, \(X_{\text{selected}}\)
11:Return\(X_{\text{selected}}\)
```  
**Algorithm 1** ANOVA feature concatenation  
The remaining network measures for each algorithm are concatenated into a single vector. These vectors are combined to form a feature matrix, which is then used as input for traditional classification machine learning models. Through an analysis of variance, we filter out the network diameter feature for both VG and LPVG. The final selected features for each algorithm are listed as follows:  
* VG: average degree (\(\alpha_{VG}\)); the maximum degree(\(M_{VG}\)); average clustering coefficient (\(C_{VG}\)); density: (\(\rho_{VG}\)); global efficiency: (\(G_{VG}\)); and average path length: (\(L_{VG}\)). * HVG: average degree (\(\alpha_{HVG}\)); the maximum degree (\(M_{HVG}\)); average clustering coefficient (\(C_{HVG}\)); density: (\(\rho_{HVG}\)); global efficiency: (\(G_{HVG}\)); network diameter (\(D_{HVG}\)); and average path length: (\(L_{HVG}\)). * LPVG: average degree (\(\alpha_{LPVG}\)); the maximum degree (\(M_{LPVG}\)); average clustering coefficient (\(C_{LPVG}\)); density: (\(\rho_{LPVG}\)); global efficiency: (\(G_{LPVG}\)); and average path length: (\(L_{LPVG}\)).