## 4 Results  
The experiments were conducted using the MATLAB(r)2022b + plugin EEGLAB v2023.1 on a Microsoft Windows 10 Pro (22H2) platform, running on an Intel(r) CoreTM i7-7500U CPU @ 2.70-2.90 GHz and a 12 GB RAM computer. The equipment was sourced from ASUS, Taipei, Taiwan. EEGLAB [29] was utilized to process the EEG data, employing the \"Clean Raw Data\" plugin for automated artifact rejection. This feature automatically flagged and excluded bad data channels and data segments unsuitable for the analysis. The process involved step-by-step training of an ML model using raw EEG data. The performance of the proposed model was evaluated using a confusion matrix [40] for each  
Figure 7: Workflow of EEG-based ASD classification: preprocessing, band decomposition, feature extraction, and SVM-based classification of hemisphere features into ASD or TD. decomposed EEG band, varying stride ratios, and individual energy windows (max, mean, and min). To enhance the model's accuracy and generalization, the ratio of the training set to the testing set is crucial in EEG data classification [41]. A large training set helps in learning complex patterns, reduces overfitting risks, and improves the parameter estimation. This approach also increases the statistical sensitivity, essential for detecting differences in clinical scenarios. Additionally, it supports strict validation, enhancing the model's stability and accuracy. The experiments used 120 epochs with a 70% (36 sample) training set and 30% (16 sample) testing set. The samples were randomly permuted before each epoch. To ensure reliability and validity, 5-fold cross-validation was applied to each epoch. Despite these measures, gender-specific patterns could still influence the outcomes, as females with ASD often exhibit subtler social deficits and differences in neural oscillation. This distribution provided accurate assessments, resulting in robust and dependable outcomes. The accuracy of the k-fold cross-validation was calculated using Equation (14), and an example of the k-fold cross-validation is shown in Figure 8.  
The confidence in the results was increased further by measuring the model's performance through various metrics, including accuracy, sensitivity, specificity, precision, G-Mean, F1-score, and the Jaccard index. These metrics were calculated using a confusion matrix [40], as shown in Table 1, and their corresponding mathematical formulas are provided in Equations (15)-(21). \[Accuracy=\frac{1}{K}\sum_{i=1}^{k}Acc_{i} \tag{14}\]  
The confusion matrix determined the number of true positive, true negative, false positive, and false negative predictions. Subsequently, the precision achieved using the \(\theta\) band was compared with that of the other bands (\(\Delta\), \(\alpha\), \(\beta\), and \(\Gamma\)). Table 2 and Figure 9 illustrate this comparison. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & & \multicolumn{2}{c}{**Predicted Class**} \\ \cline{3-4}  & & **TD** & **ASD** \\ \hline
**Actual Class** & **TD** & TN & FP \\
**ASD** & FN & TP \\ \hline \hline \end{tabular}
\end{table} 
Table 1: An example of a confusion matrix. Figure 8: An example of k-folds in cross-validation (k = 5). Where a true positive (TP) is ASD correctly classified; a true negative (TN) is TD correctly classified; a false positive (FP) is TD classified as ASD; and a false negative (FN) is ASD as classified TD. \[Accuracy(ACC.)=\frac{All\ correctly\ classified}{Total\ test\ samples} \tag{15}\]  
\[Sensitivity(Sen.)=\frac{TP}{TP+FN} \tag{16}\]  
\[Specificity(Spec.)=\frac{TN}{TN+FP} \tag{17}\]  
\[Precision\ (Prec.)=\frac{TP}{TP+FP} \tag{18}\]  
\[G-Mean=\sqrt{Sens.\times Spec.} \tag{19}\]  
\[F1\text{-}Score=\frac{2\times Prec.\times Sen.}{Prec.+Sen.} \tag{20}\]  
\[ Jaccard=\frac{TP}{TP+FN+FP} \tag{21}\]