## 6 Conclusions  
The limited size of the dataset (52 samples) may pose challenges for training a robust ML model, particularly in medical signal analysis. However, this dataset is comparable to those used in similar research studies. This study identified the best parameters for classifying ASD versus TD groups as the \(\theta\) band with a maximum energy window, a small stride, and high overlap, achieving an accuracy of 91.7% \(\pm\) 7% with the SVM-Linear classifier. While the \(\Delta\) band shows good performance in terms of the mean energy windows, it does not surpass the performance of the \(\theta\) band.  
The highest overlap ratios, combined with the maximum energy window, produced the best outcomes for early ASD detection, particularly when using the \(\theta\) band, which delivered the highest accuracy and sensitivity in this study. The use of non-linear SVM kernels further enhances the classification performance, and the \(\Delta\) band also shows promise under certain conditions. However, the \(\theta\) band remains the most accurate across all decomposed EEG bands. These results indicate that energy features from both the left and right hemispheres of the brain can be used to detect ASD, which holds significant potential for future medical applications.  
The main contribution of this study is the development of a novel EEG-based method for diagnosing ASD by analyzing the energy differences between the left and right brain hemispheres. It demonstrates that the \(\theta\) band, combined with a maximum energy window and high overlap, achieves the highest classification accuracy (91.7%) using a linear SVM. This study underscores the importance of the windowing parameters and kernel selection, offering a reliable and cost-effective approach to early ASD detection. This method also provides a foundation for future advancements using more sophisticated ML techniques.  
Future work could involve fine-tuning the stride and overlap parameters, incorporating additional features, or employing advanced methods such as a spectrogram + CNNs or multiple kernel learning or neural-network-based models to improve the classification accuracy and robustness. Such approaches could significantly aid in the early diagnosis of ASD.  
B.S.F.: data curation; software; validation; writing--reviewing and editing: visualization. M.K.S. and A.A.: conceptualization; methodology; supervision. All authors have read and agreed to the published version of the manuscript.  
This study did not receive any external funding.  
The study was conducted in accordance with the Declaration of Helsinki and approved by the Institutional Review Board of the University of Baghdad and Cukurova University, in cooperation with Medical City, Baghdad, Iraq (Reference No. MAS. 184 and date of approval: 9 January 2024).  
The hospital, participants, and participants' families provided official approvals to collect EEGs and incorporate them into the study. The names of the participants will not be disclosed.  
The datasets utilized and scrutinized in the current study are available from the corresponding author upon reasonable request.  
The authors extend their gratitude to the University of Baghdad and Medical City, especially [PERSON] and [PERSON] from the National Autism Center and Children's Welfare Teaching Hospital in Baghdad, Iraq, who confirmed the diagnoses in pure ASD labeling. [PERSON]. [PERSON], who currently works in the EEG unit, contributed to the electroencephalography recordings, leveraging her expertise as a clinical neurophysiologist. I would like to express my heartfelt gratitude to [PERSON] for his invaluable efforts and dedication in assisting me with dataset collection and methodology development.  
The authors report no conflicts of interest.