# DenseNet-Based Classification of EEG Abnormalities Using Spectrograms
## 5 Discussion  
In this study, we introduce a DenseNet-based method to classify normal and abnormal EEGs using the TUAB datasets. Unlike previous studies, which often relied on multiple EEG channels [25, 31, 32, 38], our approach focuses on channels T5 and O1. This helps mitigate the challenges associated with using multiple EEG channels. First, it simplifies the direct identification of subject-dependent reactive bands, avoiding the need for automated identification processes [39]. Second, it reduces the dimensionality of the feature vector, which can otherwise negatively affect classifier performance. Several previous studies have used the same TUAB dataset as this study to classify EEG recordings as normal or abnormal. However, their approaches vary widely in terms of which portions of the data they use. [PERSON] et al. [31] used 21 channels and excluded  
Figure 10: Example of a false negative event: (**A**) spectrogram with (**B,C**) LIME and (**D,E**) Grad-CAM visualizations (T5: top channel; O1: bottom channel), with a prediction probability of 0.166. The red box highlights the abnormal EEG recordings. Figure 9: Example of a false positive event: (**A**) spectrogram with (**B, C**) LIME and (**D, E**) Grad-CAM visualizations (T5: top channel; O1: bottom channel), with a prediction probability of 0.757. The red box highlights the abnormal EEG recordings. the first 60 s of each recording to reduce artifacts, analyzing up to 20 min of EEG and achieving 86.16% accuracy with 5-fold cross-validation. In contrast, [PERSON] et al. [2] trained a deep recurrent network (ChronoNet) using only the first minute of EEG, reporting 86.57% accuracy. Similarly, [PERSON] et al. [30] focused on the first minute across 24 channels, applying chaotic local binary pattern analysis and obtaining accuracies between 93.84% and 98.19% using SVMs. Other studies also concentrated on brief EEG segments on channel T5-O1 of TUAB EEGs: [PERSON] et al. [20] used the first 60 s from the T5-O1 channel, with error rates of 41.8% (KNN) and 31.7% (random forest); [PERSON] et al. [23] applied a CNN to the same channel (T5-O1) and duration, reaching 79.34% accuracy. [PERSON] et al. [29] used only the first 30 s with a WaveNet-LSTM model, reporting 88.76% accuracy. [PERSON] et al. [21] included up to 11 min of EEG in a CNN model, achieving 76.90% test accuracy, while [PERSON] et al. [32] removed the first minute and used up to 20 min, reporting RMSE values between 0.47 and 1.75. These studies have adopted inconsistent strategies regarding the use of the first minute of EEG recordings in the TUAB dataset. Some excluded it due to concerns about artifacts, while others used only the first minute, suggesting it is representative of the entire recording. However, these conflicting conclusions create ambiguity in the classification process. Abnormal events may or may not occur within the initial minutes, so relying exclusively on or removing the first minute risks producing inconsistent and potentially biased results. Moreover, the rationale for these choices is often unclear or insufficiently justified. Such inconsistencies undermine the comparability and robustness of model performance across studies. To address this issue and ensure a more comprehensive and consistent representation of the data, we chose to use the entire EEG recording when developing our model. In addition, these prior studies were not evaluated on an independent test set, leaving their generalizability to unseen data uncertain. Nonetheless, as most prior studies employed cross-validation, we adopted the same approach to ensure fair and consistent performance comparisons. The detailed results of our 5-fold cross-validation are presented in Table 11.  
In this work, we develop a DenseNet-based classification method using signal images, spectrograms, and scalograms as inputs, which are widely employed in EEG analysis [12; 33; 40; 41].