## 1 Introduction  
Autism spectrum disorder (ASD) is a genetic and epigenetic disorder with environmental and immune triggers, characterized by a range of manifestations. The incidence of ASD in the United States has increased from 1 in 68 children in 2014 to 1 in 36 children, with boys being diagnosed more often than girls at a ratio of 3.9:1 [1, 2]. Globally, the World Health Organization (WHO) estimates that one percent of children are affected [3], while Autism Spectrum Australia (ASA) reported a prevalence rate of 1 in 100 in 2014, which rose to 1 in 70 by 2018 [4]. Studies of high-risk infant siblings indicate that the behavioral features characteristic of autism typically emerge during the later months of the first and second years of life [5]. An impaired capacity to perform basic activities and interact with others often leads to prejudice and human rights violations [3]. New technologies in the classification of electroencephalograms (EEGs), such as signal processing and artificial intelligence (AI), have proven to be highly effective in various applications. For instance, seizures, which are a hallmark of epilepsy, are defined as irregular and uncoordinated electrical activities in the brain [6]. Research on the gender differences in emotional reactions has advanced computational models for emotion-based applications by leveraging linear and non-linear EEG characteristics [7]. Other studies have focused on developing automatic gender identification models by selecting EEG channels based on emotional signals. EEG classification techniques have also been applied to motor imagery in brain-computer interfaces (BCIs) [8] and emotional EEG categorizations [9; 10]. One study examined happiness, anxiety, and sadness through EEG signals recorded from participants watching audio-visual clips [11]. Dynamic applications have further demonstrated significant potential in EEG-based classifications. For example, movement-related EEG classifications have been explored for enhancing BCI systems. In [12], researchers used EEG signals to decode the movement intention during a reaching task, analyzing 30 movement types based on direction, distance, and target position. They demonstrated the potential for real-time control in movement-related BCIs, achieving the highest classification accuracy with premovement EEGs for direction and distance. Similarly, ref. [13] investigated movement intention decoding using EEGs to control BCIs. This study specifically focused on voluntary hand movement directions and low-frequency components (\(\leq\)6 Hz), achieving a high classification accuracy of 80% using a novel signal processing approach for four movement directions. Advancements in diagnostic methods, particularly for ASD, have significantly improved in both quality and efficiency. ASD-DiagNet is one such approach, employing functional magnetic resonance imaging (fMRI) data and enhancing the feature and model parameter quality through joint learning with an autoencoder and a single-layer perceptron. Data augmentation using synthetic datasets further strengthens this framework [14]. Another study utilized a convolutional neural network (CNN) to diagnose ASD in pediatric patients using resting-state fMRI. The CNN model incorporated convolutional, pooling, batch normalization, dropout, and fully connected layers to analyze high-dimensional data effectively [15]. Additionally, models like AlexNet, VGG16, and ResNet50 have been shown to be efficient in early ASD detection using cepstral coefficient features and fMRI data [16]. These models provide valuable insights into the functional connectivity and biomarkers of ASD pathology [17]. Despite these advances, magnetic resonance imaging (MRI) presents several challenges for children: (1) Patients may become agitated and require calming, often through medication, with potential side effects. (2) The confined space of the MRI can cause claus-trophobia and anxiety, necessitating sedation or resulting in poor-quality scans.