# A Hybrid CNN-GRU-LSTM Algorithm with SHAP-Based Interpretability for EEG-Based ADHD Diagnosis
## 2 Related Work
### Interpretability and Research Gaps  
Though there have been many studies that have used deep learning approaches for EEG-based ADHD classification, there are several important gaps, especially regarding interpretability, task-specific modeling, and the incorporation of State-of-the-Art architectures. While many approaches optimize for classification accuracy, they treat the model as a \"black box\" and provide no or limited explanation of why it made a specific prediction. Such a lack of transparency erodes clinical trust, particularly in pediatric populations where diagnostic accountability is crucial. Even though SHAP and Local Interpretable Model-Agnostic Explanations (LIME) have been taken up to render post hoc interpretability, they are usually applied externally to the modeling process rather than being intrinsic to the model design. Most models also ignore the temporal neurodynamics of cognitive control and attentional processes that are at the heart of ADHD. Consequently, their outputs lack neurophysiological underpinnings and fail to capture clinically meaningful signal change over time.  
Hybrid models, e.g., CNN-BiLSTM and CNN-GRU, have advanced towards spatiotemporal dependency modeling in EEG data, but there are still limitations related to standardized evaluation protocols and multi-level interpretability frameworks. A further major limitation is the low exploitation of transformer-based approaches, which have demonstrated State-of-the-Art performance across other biomedical time-series tasks by learning long-range dependencies and global interchannel relationships. Despite their potential, these architectures have not been systematically compared with recurrent models in the context of ADHD classification using task-evoked EEG.  
This study addresses these limitations by embedding SHAP-based interpretability directly into the modeling pipeline and systematically evaluating both recurrent (CNN-GRU-LSTM) and transformer-based models on a pediatric EEG dataset collected during attention-demanding tasks. Through rigorous benchmarking and feature attribution anal  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Ref** & **Study Focus** & **Methods** & **Key Findings** & **Identified Gaps** \\ \hline
[15] & Mental state classification with explainable DL & CNN + attention + SHAP & Achieved interpretable 91\% accuracy & General mental state decoding, not ADHD-specific \\ \hline
[16] & Spatiotemporal hybrid modeling in ADHD & CNN + BiLSTM & Outperformed conventional CNN and LSTM & No interpretability; no SHAP \\ \hline
[17] & Attention-based neural decoding & CNN + GRU + Attention & Improved detection of task-relevant EEG activity & Focused on general cognition, not ADHD \\ \hline
[18] & Importance of preprocessing in EEG classification & ICA + CNN-LSTM & Filtering and epoching improved model performance & No real-time classification \\ \hline
[19] & Task-based EEG learning for ADHD & Cognitive task EEG + CNN & Task-evoked EEG provided better discrimination & No interpretability features included \\ \hline
[20] & Transfer learning in EEG ADHD classification & Pretrained CNN + fine-tuning & Reduced training time with high accuracy (93\%) & Requires pretraining on large datasets \\ \hline \hline \end{tabular}
\end{table}
Table 1: Cont.  
ysis, the proposed framework aims to bridge the gap between high-performance neural networks and clinically interpretable diagnostics for ADHD.