# Automatic Identification of Children with ADHD from EEG Brain Waves
## 7 Discussion
Cross-validation evaluates the performance of machine learning models, and this helps to compare machine learning methods and determine which is ideal for solving a specific problem [60]. In our study, we got similar accuracy in both holdout and k-fold cross-validation, which makes our model robust against bias and overfitting. SVM has been widely used to classify EEG signals for neurological disorders [61]. It works relatively well when there is a clear margin of separation between the classes [62], and the dataset used in this study has two classes: healthy children labeled as class 1 and children with ADHD labeled as class 2. We used Gaussian kernel SVM for classification, as it has excellent learning performance and can give a reliable estimate of uncertainty. The Gaussian kernel ensures a globally optimal predictor that minimizes the estimation and approximation errors of a classifier [63]. In this study, we used statistical and time- and frequency-domain features of the four EEG sub-bands, but most prior work used nonlinear features and also worked with all the sub-bands. As mentioned in Section 2, they mostly employed KNN and neural networks. Recently, many studies have been focusing on MRI to identify neurological disorders, but compared to MRI, EEG is more flexible, affordable, and also suitable for children. We also compared the results of two cross-validations. From the result of these two cross-validations, we can say there is no bias in the dataset. In this study, we got around 93% accuracy in the SVM classifier to identify ADHD in children from 11 features extracted from each sub-band of the EEG signal. This accuracy is reasonable for classifying any EEG signals. The main challenges we faced in this study were to understand this dataset to determine the window segment and select the good features. As it is a large dataset, we selected a 2 s window segment with 50% overlap for each of the sub-bands to have as much information as possible. Windowing is used to isolate features into small segments of overall EEG data to improve feature resolution [64]. In summary, we have presented an EEG machine learning pipeline for ADHD detection, explaining each stage of the pipeline (including signal preprocessing and data preparation) with thorough explanations and rationale. We utilized only the first four sub-bands of EEG and eliminated the higher-frequency band, which reduced the computational load for the model and kept mean accuracy of 93.2%. Simple EEG features were extracted from a large dataset of 120 children, which was collected from two different sessions at two different places, eliminating measurement bias in data collection. The experimental setup was also child-friendly, easy to reproduce in local settings, and could be employed for future ADHD detection. We also performed rigorous validation to ensure that our model was not impacted by bias and overfitting, which commonly appear in the machine learning pipeline. Despite this, we need to address a few limitations of our research. 1. To improve the accuracy, we may need to evaluate more features through the use of different machine learning models for comparison of results. 2. We will also try different window sizes (0.5 s or 5 s, for example) in future studies. We will next work with only two EEG frequency bands (theta and beta), as these two have significant changes in ADHD patients, and investigate more into the sub-band of the EEG signals. * Based on our previous research on BCI interfacing [11; 65] and EEG channel reduction [66], we will develop a portable solution with reduced EEG channels to evaluate the possibility of real-time ADHD detection.