## 5 Discussion  
These results demonstrate that the selection of the energy window, stride, and SVM kernel significantly impacts the classification performance. The \(\theta\) band emerges as the most effective for differentiating between ASD and TD groups, particularly with shorter strides (a greater overlap), suggesting its ability to capture the neural oscillations most closely associated with ASD. An SVM with a linear kernel outperforms non-linear kernels (the RBF and quadratic kernels) under conditions with small strides and the maximum energy windows. This highlights the effectiveness of the linear kernel in well-defined feature spaces, such as the \(\theta\) band. However, non-linear kernels, such as the RBF and quadratic kernels, provide valuable insights across various frequency bands. While the \(\theta\) band remains the most informative, the \(\Delta\) band also shows potential, particularly with non-linear kernels. This suggests that more complex decision boundaries are better suited to capturing the EEG patterns associated with ASD. As shown in Table 3, comparisons indicate that higher levels of windowing improve the classification performance across all SVM kernels. A greater overlap (smaller strides) enhances the feature extraction, resulting in better ASD detection. Conversely, less overlap (larger strides) significantly decreases the performance across nearly all frequency bands. When compared to existing studies, our results are competitive. For instance, the current study's accuracy of 91.7% \(\pm\) 7% is higher than the accuracy reported in [23] (88.6%) and comparable to the range reported in [18] (83-86%). The use of an SVM with energy features, combined with windowing techniques, has proven to be an effective approach to differentiating between ASD and TD. Our classification performance also surpasses that of previous studies, such as those using statistical and spectral features combined with methods like the CSTO-DRN approach, which reported accuracies ranging from 83% to 86% in [18]. Furthermore, studies employing fMRI- and CNN-based architectures, such as VGG16 and ResNet50, achieved accuracies between 63.4% and 87% in [17]. These comparisons highlight the effectiveness of the proposed method, particularly the application of an SVM with energy features, windowing strategies, and high overlap ratios for early ASD detection. The concept of comparing the two hemispheres of the brain is inspired by findings suggesting that the right hemisphere is more active in psychotherapy [42]. The right hemisphere is responsible for implicit, nonverbal, intuitive, and integrative processing of \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Source**} & **Test** & **Male (No.)** & **Age** & **Method** & **Accuracy Mean \%** \\  & **method** & **Female (No.)** & **Years** & & \(\pm\)**SD \%** \\ \hline \multirow{4}{*}{Current study} & GARS & ASD: & & & \\  & CARS & M (18); F (4) & ASD: 4.85 \(\pm\) 1.5 & \multirow{2}{*}{\
\begin{tabular}{c} Energies \\ Windowing \\ SVM \\ \end{tabular} } & \multirow{2}{*}{\(91.7\pm 7\)} \\  & DSM-5-TR & TD: & TD: 5.9 \(\pm\) 3.5 & \multirow{2}{*}{
\begin{tabular}{c} Findings \\ SVM \\ \end{tabular} } & \multirow{2}{*}{\(91.7\pm 7\)} \\  & Medical history & M (18); F (12) & & \\
[23] & ASD: 28 & ASD:18–68 & Statistical and spectral & \\
2024 & Not available & TD: 28 & TD: 18–68 & features+CSTO-DRN & \\  & PANS & ASD: & & & \\
[18] & DSM-V & M (12); F (6) & ASD: 3.92 \(\pm\) 1.38 & Statistical and spectral & 83 \(\pm\) 14 \\
2023 & Medical history & TD: & TD: 3.84 \(\pm\) 1.24 & brain connectivity & 0 \\  & Professional & M (12); F (6) & & & 86 \(\pm\) 18 \\
[17] & Two datasets (ABIDE I and ABIDE II)–images/5–64 years &
\begin{tabular}{c} FMRI+CNN \\ VGG16, ResNet50 \\ \end{tabular} & 63.4 –87.0 \\
[19] & DSM-V & M (13); F (2) & ASD: 10.4 & MS-ROM/I-FAST, \\
2017 & ADOS scale & TD: & TD: 9.2 & TWIST + ANN & 84–92.8 \\  & M (4); F (6) & & & \\ \hline \hline \end{tabular}
\end{table}