## 1 Introduction
(3) Long scan durations are challenging for children who struggle to remain still, leading to motion artifacts and degraded image quality. (4) The noise generated by MRI machines often frightens children, even with ear protection. (5) The limited availability of MRI facilities can delay diagnosis, particularly in regions without pediatric services. (6) The contrast agents used in MRI can cause complications, especially in children with renal issues. (7) The high cost of MRI scans imposes financial strain on families, particularly when frequent scans are required. EEGs offer a less invasive, safer, and more cost-effective alternative to MRI for children, including those with autism. However, the current EEG screening methods, which rely on resting-state data, may generate redundant information. Optimizing the EEG analysis could address these limitations and further enhance its diagnostic utility. Related WorksThere is a need to draw attention to the developments in the analysis of EEGs, particularly in relation to ASD. Previous research has employed statistical methods and machine learning (ML) to improve the analysis of EEG features and enhance the accuracy of ASD classification. Below are some of the key contributions in this area:  
One study used a statistical analysis and ML to enhance the EEG feature analysis and verify the model predictions, revealing a specific subpopulation of children with ASD based on the phase-locking value (PLV) and the corrected imaginary part of the phase-locking value (ciPLV). The accuracies achieved were 83%, 83%, and 86%, respectively [18]. Another study utilized EEG data from children with ASD and typically developing (TD) children and applied ML classifiers to distinguish ASD cases from controls based on artifact-free EEG segments. This approach demonstrated 100% predictive accuracy using various ML systems with a training-testing protocol and 84-92.8% accuracy using the leave-one-out protocol [19]. Further research has focused on preprocessing, segmentation, and time-frequency distribution (TFD) techniques, such as short-time Fourier transformation (STFT), continuous wavelet transformation (CWT), and the smoothed pseudo-Wigner-Ville distribution (SPWVD) [20]. These techniques generated spectrograms, scalograms, and SPWVD-TFD images, which were integrated into ResNet-101 and DenseNet-121 pre-trained models and fed into a proposed ASD-Net. Deep learning models classified ASD and TD participants based on the TFD images. The SPWVD-TFD combined with the ASD-Net model achieved a mean accuracy of 97.35%. Compared to the benchmark DenseNet-121 and ResNet-101 models, the proposed CNN model with five convolution layers required fewer learnable parameters and demonstrated computational efficiency and speed. Other ML techniques, including random forest, K-nearest neighbors, and support vector machines (SVMs), have also been used to forecast ASD meltdowns [21]. In one study, cepstral coefficient features were utilized to evaluate AlexNet, VGG16, and ResNet50 for ASD detection. Spectograms from cepstral coefficients, combined with a modified AlexNet architecture, achieved 90% accuracy using linear frequency cepstral coefficients (LFCCs). The proposed AlexNet with LFCCs reached 85.1% accuracy, demonstrating the efficiency of specialized networks for accurate ASD categorization. Another group emphasized the importance of early ASD diagnosis using biological markers [22]. EEG signals filtered to isolate the Gamma band were used to differentiate between ASD and TD participants based on the power spectral density (PSD) and phase-locked values (PLVs). An ANOVA validated significant differences in these features. In another approach, the researchers developed an efficient ASD diagnostic model using EEG signals [23]. After noise removal with Gaussian filtering, the statistical and spectral features were extracted and input into a deep residual network (DRN). The model employed chronological sewing training optimization (CSTO) to enhance performance.