## 6 Discussion and Comparison  
We developed an automated deep-learning-based method to classify people with schizophrenia and healthy controls. We tested different hyperparameter choices to build a robust backbone CNN model. The designed model has a pyramidal architecture, nine convolutional blocks, four pooling layers, and two fully connected layers. Each convolutional block consists of a convolutional layer, batch normalization, and ReLU activation. Cross entropy was used as a loss function, and Adam was selected for optimization. To show the effectiveness of the proposed model, we considered four models with different architectures and statistically analyzed their performances using the Wilcoxon signed-rank test. We found that the model is significantly better than other models.  
The data augmentation scheme effectively helps the model learn the features from a relatively small amount of data. We found that a three-second temporal length and 0.125-s stride are the best for generating trials to train the model with good generalization and avoiding overfitting.  
The extracted features by the CNN model can distinguish the EEG signal trials of 3 s into schizophrenic and healthy controls. To show the discriminability of the features, we analyzed them using box plots, which show that the features for two classes have small intra-class variations and high inter-class separation. Further, the correlation analysis of features with frequency bands reveals that beta and gamma bands are negatively correlated  
Figure 7: Effect of each extracted feature on EEG signals. In healthy control, first and third features are dominant whereas the second and fourth features are dominant in schizophrenia class.  
with the schizophrenia class. Neurologists often examine parameters such as power spectral densities, coherence, and connectivity patterns within specific frequency bands known to be altered in schizophrenia. Our study aligns with these practices by focusing on the beta and gamma bands, which our findings suggest are significantly correlated with the schizophrenia class.  
In discussing the clinical implications of our findings, we explored potential associations between the EEG features identified and schizophrenia symptoms, both positive and negative. Our findings suggest that certain EEG patterns, particularly in the beta and gamma bands, may be more prevalent in patients exhibiting pronounced negative symptoms, such as blunted affect and social withdrawal.  
We compare the performance of the proposed method with that of the state-of-the-art techniques for schizophrenia detection, see Table 10. For comparison, we selected the most recent methods [11; 14; 15; 16], which are based on deep learning and were evaluated on the same dataset. From Table 10, we can see that the proposed method outperforms the most recent works in terms of accuracy, sensitivity, and specificity scores.  
Our findings suggest that EEG patterns in the beta and gamma bands are indicative of negative symptoms in people with schizophrenia. This observation is supported by both the literature [36; 37] and our analysis of EEG signals from the patient group, where these patterns were predominantly observed. This link is significant as it aligns with neurobiological insights into the cortical dysfunctions typically seen in schizophrenia.