# Prediction of Beck Depression Inventory Score in EEG: Application of Deep-Asymmetry Method
## 4 Discussion  
Early diagnosis and treatment of depression is important. It must also be closely diagnosed objectively and consistently. Depression can be treated with simple counseling and drug treatment, depending on the severity. However, patients are especially reluctant to show their condition to a specialist. Socially negative perceptions of psychiatry [47] and circumstances such as the COVID-19 pandemic make depression patients reluctant to seek professional medical help. As EEG devices become more popular and readily available, the deep-learning-based methods discussed in this study can be used as powerful tools for the early diagnosis of depression. Another limitation of diagnosing psychological depression is that the symptoms of depression vary from person to person. A phenomenon such as masked depression [8], in which symptoms of depression appear as symptoms of diseases other than depression, increase the risk of misdiagnosis. We presented an effective BDI prediction model to represent the degree of asymmetry, a major biomarker of depression, on a matrix image. This EEG-based deep learning method more objectively determines the severity of depression, suggesting that it can also be used as a CDSS to help professionals make decisions. Notably, there are very few similar EEG-based models, especially with regard to depression severity determination. Interpreting the results of this study reveals that the deep-asymmetry methodology, which showed good performance in the existing classification model, also showed satisfactory performance in the regression model predicting the BDI, a measure of depression severity. In previous studies [13,27], it was more effective to use the EEG as linear time-series signal data in 2D image form than to use it for deep learning model training. This study goes beyond proving that the EEG imaging method is effective in solving the classification problem that distinguishes brain disease patients from healthy controls in previous studies and reveals that the EEG imaging method is also effective in solving the regression problem of predicting a specific severity score. Also, we compared the STFT spectrogram method and deep-asymmetry method, which are the most commonly used EEG imaging methods. The results also obtained good results in the regression model in which the deep-asymmetry methodology predicts the severity of depression. To interpret the reason, I think that it is because the effective feature extraction of EEG was accomplished by imaging asymmetry, which is a major characteristic in depression. Among the various results of this study, the predictive performance corresponding to the delta band is noteworthy. In the conventional deep-asymmetry classifier, the image in the alpha band showed the best performance, but in the regression model predicting the BDI score, the performance in the delta band was the best, and the performance in the alpha band was the second-best. Furthermore, we reviewed the results of existing EEG studies to verify the results of this study. First, in a study by [PERSON] et al., it was found that the asymmetry of EEG changes according to the severity of the symptoms of depression and anxiety in cases of major depression [48]. [PERSON] et al. suggested that asymmetry of relative delta power appeared in the depressed group [49]. Delta power has also been shown to be associated with psychological distress in depressed patients [50]. Taken together, the results of the model in our study support the results of previous studies. We reviewed the recently emerging cross-frequency coupling (CFC). CFC is emerging as a basic function of brain activity that correlates with brain function and dysfunction [51]. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Band** & **RMSE** & **MAE** \\ \hline Delta & 3.46 & 2.23 \\ Theta & 5.09 & 3.40 \\ Alpha & 4.17 & 2.76 \\ Beta & 4.45 & 2.83 \\ Broadband & 8.51 & 6.77 \\ \hline \hline \end{tabular} The result value is expressed as the average value of 5-fold validation.  
\end{table}