# A Hybrid CNN-GRU-LSTM Algorithm with SHAP-Based Interpretability for EEG-Based ADHD Diagnosis
## 5 Discussion  
### Comparison with Previous Studies  
This study aimed to address key challenges in EEG-based ADHD classification by proposing and evaluating a suite of deep learning architectures that integrate spatial, temporal, and global contextual information from multichannel EEG recordings. Compared with existing approaches relying on handcrafted features [11] or spatial-only CNN models [12], our results demonstrate that combining multiple deep learning paradigms--specifically, convolutional, recurrent, and attention-based modules--significantly enhances classification performance and interpretability, particularly when applied to task-evoked EEG signals. The suggested CNN-GRU-LSTM architecture significantly outperformed the baseline CNN-LSTM model on all evaluation metrics. That is, it recorded an average accuracy of 99.58% versus 92.36%, F1-score of 0.9998 vs. 0.919, and AUC of 0.9963 vs. 0.941, respectively. The significant improvement margins are a testament to the hybrid model's capacity to learn both short- and long-term temporal dependencies simultaneously through GRU and LSTM layers, respectively, while the CNN components capture localized spatial features effectively. These findings are consistent with earlier studies by [PERSON] et al. [16] and [PERSON] et al. [17], which emphasize the diagnostic value of modeling EEG's spatiotemporal structure. In contrast, although the transformer encoder (2\(\times\) and 3\(\times\)) models achieved moderately high performance during cross-validation, they exhibited poor generalization and reduced clinical reliability, with accuracy ranging from 70% to 77%, F1-scores ranging from 0.3692 to 0.4465, and AUC values between 0.7838 and 0.8570. These results were accompanied by low specificity (as low as 0.788) and unstable performance across test folds, highlighting a tendency toward overfitting and sensitivity to partitioning. The spatiotemporal transformer solved some of these problems with the addition of 2D positional encoding. It attained a more balanced classification with an accuracy of over 99.98%. Also, F1-scores were nearly 0.9999, and AUC values were around 1.000. This confirms that the model can capture spatial topography and subtle temporal patterns in EEG signals. The most stable results and consistent diagnoses were obtained using the hybrid CNN-RNN-transformer model with incorporated CNN, GRU-LSTM, and transformer blocks, which were unified into a single framework. This model provided the best performance on all metrics, achieving accuracy above 99.98%, F1-scores greater than 0.9999, and perfect precision and MCC. This model's performance also proved its reliability across various data splits in comparison to the high variance seen in transformer-only models. These findings prove that architectural hybridization allows for the integration of spatial, temporal, and global contextual learning adaptation, providing a clinically practical and transparent solution for ADHD classification based on EEG signals. The interpretability analysis using SHAP values further reinforced the relevance of specific EEG channels (Fp1, F8, and P4), consistent with prior findings on the involvement of frontal and temporoarietal regions in attention regulation and executive function in ADHD [4; 5; 6]. Moreover, regularization components, such as dropout and batch normalization, were shown to enhance SHAP stability, contributing to model generalization and transparency. Compared to prior studies using opaque deep learning models or lacking post hoc explanation methods [13; 14], our work contributes a more interpretable and clinically aligned diagnostic framework. Additionally, by using task-evoked EEG data, the present study builds on the observations of [PERSON] et al. [19], who emphasized the value of cognitive-load paradigms for eliciting diagnostically relevant neural responses.