# A Hybrid CNN-GRU-LSTM Algorithm with SHAP-Based Interpretability for EEG-Based ADHD Diagnosis
## 2 Related Work  
### Deep Learning and EEG-Based ADHD Classification  
Recent advances in EEG-based ADHD diagnosis have been directed towards the integration of advanced signal processing techniques and deep learning to enhance diagnosis performance. [PERSON] et al. [11] used entropy-based features and a random forest classifier to differentiate between children with ADHD and their TD peers. [PERSON] et al. [12] proposed a CNN model trained on time-frequency representations of EEG signals, achieving stable results across sessions. [PERSON] and [PERSON] [13] introduced a novel multimodal method involving skeletal motion features, indirectly supporting ADHD detection through motor analysis. Other researchers have prioritized model interpretability: [PERSON] and [PERSON] [14] and [PERSON] and [PERSON] [15] employed SHAP and LIME to increase the transparency of EEG classifiers. Building on this, recent works have explored hybrid deep learning models, such as CNN-BiLSTM and CNN-GRU architectures. [PERSON] et al. [16] reported improved classification performance with spatial-temporal modeling, while [PERSON] et al. [17] integrated attention mechanisms for EEG-based mental state recognition. [PERSON] et al. [18] emphasized the importance of preprocessing, using ICA and band-pass filtering before classification with CNN-LSTM models. [PERSON] et al. [19] demonstrated that training models on task-evoked EEG (rather than resting-state data) led to better cognitive differentiation in ADHD. Finally, [PERSON] and [PERSON] [20] introduced a data-efficient approach using transfer learning to mitigate small dataset limitations in clinical neurophysiology studies. Despite these improvements, gaps remain in the integration of interpretable hybrid models tailored to cognitive task conditions. Table 1 summarizes key findings and open issues from the literature.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Ref** & **Study Focus** & **Methods** & **Key Findings** & **Identified Gaps** \\ \hline \multirow{2}{*}{[11]} & Entropy-based feature extraction & Random forest classifier & Achieved 97.53\% accuracy & No interpretability; lacks temporal modeling \\ \hline \multirow{2}{*}{[12]} & Time–frequency EEG representation for ADHD & Wavelet transform + deep CNN & Robust session-level accuracy & Spatial focus only; no temporal dynamics \\ \hline \multirow{2}{*}{[13]} & Neuro-motor pattern analysis & Two-dimensional tracking + CNN & Identified motor planning deficits & EEG used indirectly; no EEG dynamics analyzed \\ \hline \multirow{2}{*}{[14]} & Explainable AI in ADHD detection & SHAP, LIME, saliency maps & Enhanced clinician interpretability & Not combined with deep models \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of related studies on EEG-based ADHD classification.  
As shown in Table 1, recent advancements in EEG-based ADHD classification have significantly improved diagnostic performance through the application of deep learning and hybrid models. However, many studies either lack interpretability mechanisms or are limited to specific EEG representations, such as spatial-only or resting-state data.