# DenseNet-Based Classification of EEG Abnormalities Using Spectrograms
## 5 Discussion
In contrast, Figure 10, a false negative event, depicts a case where high power at lower frequencies (indicated by the red portion in the spectrogram, Figure 10A) enabled the model to accurately classify the event as normal. However, it is important to note that not all abnormal spectrograms contain detectable abnormal events, resulting in potential misclassifications, particularly false negatives. To address these issues, visualizations produced by LIME (Figure 10C) and Grad-CAM (Figure 10E) can assist researchers in manually correcting such misclassifications. Additionally, we propose a post-processing method in this study aimed at reducing false classification events. A limitation of the current work is the potential impact of artifacts in the EEG recordings on model performance. While the TUH EEG dataset provides a valuable resource, it may still contain various types of noise and artifacts, such as eye blinks, muscle activity, or electrical interference, which could compromise the quality of the signals. These artifacts, if not properly mitigated, could lead to misclassifications or reduce the overall performance of the model. Although our method includes a post-processing step to average predictions across multiple image outputs, the presence of artifacts in certain segments could still result in false positives or false negatives. Future work should explore more robust artifact removal or detection techniques, such as adaptive filtering or artifact subspace projection, to further enhance the model's accuracy and reliability in clinical applications. Another limitation of the current study is that our method was developed and tested only on the TUH EEG dataset. While the results are promising, the model's performance and generalizability across other EEG datasets or real-world clinical settings remain untested. Variations in data acquisition protocols or patient populations could affect the model's effectiveness. To enhance robustness and generalizability, future work will involve validating the method on diverse EEG datasets and in real-world clinical environments. This will help assess how well the method generalizes to different populations and recording conditions. Additionally, in future work, we will explore the impact of different EEG channel combinations on model performance, and further analyze the relative importance of each channel in abnormal EEG detection. Moreover, future work would focus on testing the proposed method using more specific datasets to determine its ability to distinguish disease symptoms or differentiate between distinct neurological diseases. For example, datasets related to epilepsy type classification, seizure detection, or seizure prediction could be employed to assess the method's effectiveness in identifying disease-specific patterns. This will be essential for validating the model's generalizability and clinical applicability. Furthermore, it is important to note that certain EEG events may be visible in the raw signal but not apparent in the spectrogram or scalogram, potentially affecting model performance [12]. To improve effectiveness and clinical applicability, we plan to integrate raw EEG signals with spectrograms or scalograms in future work, either through input fusion or decision-level ensemble methods. In addition, to ensure the successful integration of this model into clinical practice, several challenges must be addressed. Real-time deployment in clinical settings requires effective handling of artifacts, rapid processing times, and minimizing false positives and false negatives to avoid clinical misinterpretation. The model's interpretability is also critical for clinical adoption. Techniques such as LIME and Grad-CAM provide transparency into the model's decision-making process, which is essential for fostering clinician trust. However, explainability methods may themselves produce inconsistent or overly complex outputs, which could confuse rather than assist clinicians. Therefore, co-designing interpretability outputs with domain experts is crucial.