## 4 Results
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Delta** & **Theta** & **Theta** & **Beta** & **Gamma** \\ \hline Acc. & 70.1 (10.1) & 91.7 (7.0) & 82.7 (9.8) & 64.6 (11.8) & 58.4 (12.3) \\ Sen. & 73.2 (18.4) & 91.4 (9.7) & 79.7 (13.1) & 52.5 (18.2) & 38.6 (13.7) \\ Spec. & 67.1 (15.5) & 92.1 (8.6) & 85.8 (12.2) & 77 (15.0) & 78.2 (18.0) \\ Prec. & 70.8 (12.1) & 92 (9) & 86 (11.6) & 70.2 (18.3) & 68.8 (20) \\ G-Mean & 69.5 (11.3) & 91.2 (6.9) & 82.2 (10.6) & 61.8 (13.9) & 55.5 (11.7) \\ F1-Score & 70.1 (12.3) & 91.6 (6.4) & 81.8 (11.3) & 58.3 (16.5) & 49.5 (13.8) \\ Jaccard & 55.3 (13.9) & 85.2 (10.6) & 70.7 (15.8) & 43 (16.1) & 34 (12.3) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Results for each decomposed EEG band using maximum energy windows with an 87.5% overlap ratio and SVM-L, presented as mean% (SD%). Figure 9: Classification results for each decomposed EEG band using maximum energy windows with an 87.5% overlap ratio and SVM-Linear, reported as mean% (SD%). The analysis in Table 3 compares the performance of different SVM classifiers (linear, the RBF, and quadratic) across various configurations of energy windows (max, mean, and min) for EEG-based classification. This study evaluates these configurations using five EEG frequency bands (\(\Gamma\), \(\beta\), \(\alpha\), \(\theta\), and \(\Delta\)) with the overlap ratios varying from 87.5% to 12.5%, corresponding to strides of 250 to 1750 samples:  
* The theta band's superiority: The \(\theta\) band consistently delivers the best classification results, particularly with the SVM-Linear classifier. Using a maximum energy window with a stride of 250 samples (87.5% overlap), the accuracy reaches 91.73% \(\pm\) 7%, the highest among all of the configurations. This highlights the effectiveness of the \(\theta\) band for early ASD detection. * Delta band performance: The \(\Delta\) band demonstrates strong results, particularly with the mean energy windows. For instance, using the SVM-Linear classifier, the \(\Delta\) band achieves an accuracy of 84.3% \(\pm\) 8.7% with a mean energy window at a stride of 250 samples. Similarly, with the SVM-Quadratic classifier, the \(\Delta\) band achieves an accuracy of 85% \(\pm\) 9.2% under the same conditions. Despite these strong results, the \(\Delta\) band does not surpass the superior performance of the \(\theta\) band. * Impact of the overlap and energy window settings: Higher overlap ratios and the maximum energy window generally enhance the classification accuracy, particularly for the \(\theta\) band, by capturing more details in the EEG signals. The \(\Delta\) band remains relatively stable across configurations, indicating its robustness and usefulness. However, the \(\theta\) band consistently delivers the highest accuracy. * Non-linear SVM kernels: Using non-linear kernels such as the RBF and the quadratic kernel improves the classification accuracy across the frequency bands. The \(\Delta\) band performs reasonably well with the SVM-RBF kernel, achieving up to an 82.7% \(\pm\) 9.2% accuracy in the mean energy window at a stride of 1000 samples (50% overlap). The Gamma (\(\Gamma\)) band, however, shows a lower overall performance, with its best accuracy of 73.3% \(\pm\) 10.5% achieved using SVM-Quadratic at a stride of 500 samples (75% overlap) in the minimum energy window. * Optimal configuration for ASD detection: The highest accuracy for early ASD detection is achieved with the \(\theta\) band, the maximum energy window, a stride of 250 samples, and a window size of 2000 samples (selected experimentally), classified using SVM-Linear. This configuration results in an accuracy of 91.7% \(\pm\) 7%, a sensitivity of 91.4% \(\pm\) 9.7%, and a lower standard deviation (SD) compared to the other EEG bands, as shown in Table 2 and Figure 9.  
Compared to this study, previous EEG-based ASD detection classification studies involved diverse participant groups and employed various classifiers and feature selection techniques.