## 5 Conclusions  
However, several challenges were identified:  
* **Data scarcity and imbalance:** Despite access to a relatively large EEG database, only 827 segments were usable after filtering, limiting model generalization. Furthermore, certain models showed skewed performance (e.g., perfect FPR but low AUC), likely due to class imbalance or overfitting.
* **Interpretability of deep models:** While deep models like Transformers and CNNs yielded competitive results, their black-box nature raises concerns in clinical applications where decision transparency is critical.
* **Variability in signal quality:** EEG signals are inherently noisy and patient-specific. Although normalization was applied, inter-patient variability remains a key obstacle, potentially impacting model robustness in broader clinical settings.
* **Computational Trade-offs:** Some models, such as the Transformer-based approach, offer competitive accuracy with reduced prediction time, making them suitable for real-time use. Others, while more accurate, require more extensive computation or more complex preprocessing, which may limit deployment in resource-constrained environments.  
The integration of discrete wavelet transform (DWT) with deep learning models led to significant improvements in performance. Notably, the combination of DWT with a  
Figure 6: False positive rate (FPR) across models. Lower values indicate better robustness against false alarms.  
Fourier neural network (FNN) achieved the highest performance across all metrics. This configuration recorded an accuracy of 98/96, an AUC-ROC score of 1.0, and an FPR of 0, with the shortest prediction time of 5 units. These results indicate a highly effective model capable of rapid and precise EEG signal classification.  
In contrast, the use of raw EEG data with a CapsNet model, while achieving a reasonable accuracy of 95/7, resulted in the highest false positive rate (0/127) and the longest prediction time (30 units), underscoring the limitations of bypassing signal preprocessing. Other DWT-based approaches, such as DWT + SVM and DWT + hybrid AE+CNN, also demonstrated solid accuracy and low false positive rates, though they did not surpass the FNN model. Meanwhile, the STFT + Transformer-based method showed moderate accuracy (94/6) and AUC-ROC performance but fell short in comparison to the top-performing DWT-based configuration. The experimental results confirm that applying discrete wavelet transform (DWT) as a preprocessing step significantly enhances the effectiveness of deep learning models for EEG signal classification. Among the tested methods, the DWT + FNN configuration stands out for its superior accuracy, perfect AUC-ROC score, zero false positives, and minimal prediction time. These findings highlight the potential of combining multi-resolution signal analysis with lightweight neural architectures to develop robust and efficient EEG-based diagnostic and brain-computer interface systems.  
H.S.K.: Responsible for the implementation of the model and initial manuscript drafting. M.A.: Led the development of the mathematical modeling framework and performed validation and verification of the results. G.H.J.: Conducted result analysis and contributed to the generation and refinement of visual figures. H.M.: Performed detailed dataset analysis and preprocessing. F.F.: Conceived and defined the structure and characteristics of the dataset used in the study. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors would like to sincerely thank the anonymous referees for their careful reading, insightful comments, and valuable suggestions, which have greatly improved the clarity, quality, and overall presentation of this paper.  
The authors declared that they have no conflict of interest.