# Automatic Identification of Children with ADHD from EEG Brain Waves
## 1 Introduction  
Attention deficit hyperactivity disorder (ADHD) is a behavior disorder characterized by intention, impulsivity, and in some cases hyperactivity, typically diagnosed in childhood [1]. It is a common childhood developmental disorder. The symptoms of ADHD start before age 12, and in some children, they are noticeable as early as three years of age [2]. The prevalence of ADHD has been estimated at approximately 12.1% among boys and 3.9% among girls [3]. About 6.4 million American children aged 4-17 have been diagnosed with ADHD [4]. ADHD makes it difficult for children to develop the skills to control their attention, behavior, emotions, and activities. As a result, they often act in ways that are hard for parents to manage [5]. Persistent adult ADHD may cause serious long-term consequences, such as poor academic achievement and job performance, increased risk of antisocial behavior, and drug and alcohol abuse [3]. Hence, early detection of this disorder is of great value [6, 7]. EEG is a reliable method that provides information about the background activity of the brain and indexes the substrate of cognition and behavior, shown in Table 1[8]. Therefore, it can be a useful tool for investigating and diagnosing the abnormal behavior of ADHD children. [PERSON] conducted the first abnormalities study by EEG signals in ADHD in 1973. He found that theta (Table 1) activity increased and also beta (Table 1) power dramatically reduced in ADHD [9]. Most patients with ADHD have a common brain-wave pattern that consists of an abundance of slow (delta or theta) brain waves and a shortage of fast (beta) brain waves. This means that they have a high theta-to-beta ratio [10] that could be employed for automatic recognition from the characteristic brain wave. Recently, researchers have been working with fMRI (functional magnetic resonance imaging) and MRI to identify ADHD. This is a fast-developing and complex research domain [13]. [PERSON] et al. [14, 15] reported decreased activation in the ADHD group in mesial and lateral prefrontal areas in the right hemisphere and the cingulate gyrus by using fMRI. On the other hand, EEG is quicker, affordable, portable, and gives accessible insights into brain function. Therefore, EEG can be a useful gadget for investigating and diagnosing the abnormal behavior of ADHD children. Even with the current progress, using EEG tests for ADHD detection needs a more precise approach in this area to get more accurate results. The amount of information in EEG signals is vast. It is also complicated for a human to detect abnormalities manually. This is where machine learning (ML) can be useful. Generally, ML is programming computers to optimize a performance criterion using example data or experience [16], which could be employed for the current task. This study thus aims to automatically identify ADHD children employing machine learning techniques. Previously, most studies used nonlinear features of the EEG signal and used KNN or neural networks for classification (discussed in Section 2). In this study, we have extracted statistical, time-domain, and frequency-domain EEG features; used PCA to select the best features; trained a Gaussian SVM classifier on the selected features; and employed two cross-validation methods: holdout and k-fold cross-validation to validate classifier performance. Since ADHD's behavior can be caused by differences in brain function, we worked with only four sub-bands: delta, theta, alpha, and beta frequency. The overall contributions of this work can be summarized as follows. 1. An EEG ML pipeline is presented for ADHD detection, explaining each stage of the pipeline (including signal preprocessing and data preparation) with thorough explanations and rationale. 2. Unlike other studies, we employed only the first four sub-bands of EEG, eliminating signals more than 30 Hz and thus reducing the computational load for ML model training while keeping mean accuracy of 93.2%. 3.