# A Hybrid CNN-GRU-LSTM Algorithm with SHAP-Based Interpretability for EEG-Based ADHD Diagnosis
## 4 Results
### Exploratory Analysis and EEG Feature Visualization  
To gain insights into the neurophysiological patterns associated with ADHD, an initial exploratory analysis of raw EEG signals was performed using data collected during a visual cognitive task. The task involved showing children animated figures and requiring them to count from 5 to 16, which required visual processing and executive processing. Real-time neural responses that reflect cognitive workload and attentional dynamics were captured throughout the task using EEG signals, and this was carried out for both ADHD and TD children to examine differences in signal amplitude. Particular emphasis was placed on identifying distinct channel-level patterns and temporal fluctuations that may distinguish ADHD-related neural processing. To further enhance signal interpretability, ICA was applied, allowing the extraction of neurologically relevant components by minimizing noise and ocular artifacts. Each entry in the dataset represents a single EEG instance comprising time-series values from 19 electrodes, along with a class label and unique identifier. This structured format supports the application of deep learning models and facilitates the identification of discriminative EEG features for automated ADHD classification. Figure 12 presents a multichannel representation of EEG signals characteristic of a TD child from the control group. The EEG recording reflects the brain's electrical activity and demonstrates relatively stable and balanced patterns typical of children without signs of ADHD. A visual analysis of the signals reveals a smooth distribution of amplitude-frequency characteristics without abrupt spikes or pronounced variability, indicating stable neural network function. Unlike the recordings of children with ADHD, the EEG of TD participants exhibits more uniform rhythm alternation and an absence of significant tran  
sitions between frequency ranges. This suggests stable cognitive regulation and effective coordination between different brain regions. The analysis of the EEG recording from a TD child in the control group indicates stable neural network function and balanced cognitive regulation. The amplitude characteristics of the signals remain moderate, without frequent or pronounced spikes, reflecting uniform central nervous system activity and the absence of pathological hyperactivity. The lack of significant transitions between channels and the smooth variations in frequency-amplitude characteristics confirm effective cognitive regulation and attentional control. Unlike the recordings of children with ADHD, the EEG of TD participants demonstrates signal integrity and predictable dynamics, indicating normal neural connectivity functioning. Figure 13 presents the results of ICA performed on the electrophysiological data of a TD participant from the control group. The topographic maps illustrate the spatial distribution of independent component activity, allowing for the visualization of contributions from different sources on the scalp and distinguishing accurate neural signals from artifacts such as eye movements or muscle activity. An orderly structure of topographic maps is observed in the group of TD participants without distinct pathological patterns, indicating stable functional brain activity. The extracted components are common rhythms of the population at large, such as a prominent alpha rhythm in the occipital area associated with resting and focused attention. The application of ICA has the potential to refine EEG analysis, tighten diagnosis, and allow more effective noise elimination and, as such, is an important tool for research or clinical applications. Figure 12: Raw EEG signals for control class. Figure 13: Topographic maps of ICA components for EEG in control class. Figure 14 presents an EEG recording, which reveals that the electrical activity of the brain in a child with ADHD can be represented in multiple channels.