# Prediction of Beck Depression Inventory Score in EEG: Application of Deep-Asymmetry Method
## 4 Discussion

Table 7: BDI regression performance of deep-asymmetry model. In previous studies, gamma oscillations as a biomarker in major depression were cited as the reason for CFC [52]. Of particular interest, there is a case in which frontal delta-beta cross-frequency coupling can be an indicator of social anxiety and stress [53]. In addition, there is a case of a change in the cross-frequency coupling between delta and beta oscillations during cognitive behavioral therapy for social anxiety disorder [54]. The great BDI prediction performance of the delta band in the deep-asymmetry methodology shown in our study can be said to be a very meaningful result when the above results are taken together. We also reviewed inter- and intra-subject variability. The human brain exhibits functional diversity, such as brain anatomical factors, depending on the environment and genetic factors for each individual [55]. In addition, the dynamic properties of the brain change over time, even within specific experiments [56]. This is also a major factor affecting performance, by diluting important patterns observed in EEG and making it difficult to find covariates between groups [57]. Therefore, it is very important to perform detailed feature extraction considering inter- and intrasubject variability in BCI research. Machine learning-based analysis is presented as the main method considered to overcome intra- and inter-subject variability in existing studies [58; 59]. In addition, the deep learning method was effective on challenging data sets with large inter- and intra-subject variability [60]. Therefore, the deep-asymmetry imaging method proposed by us is valuable in that it extracts the EEG features of individuals by imaging the asymmetry features and proposes a regression model that predicts the individual's BDI through the deep learning method. Table 8 summarizes other methodologies and results of BDI prediction similar to our study. The results of these studies were compared using the commonly used RMSE indicator. As shown in the table, the deep-asymmetry method proposed in this study showed a strong performance compared with other studies. This demonstrates that the deep-asymmetry method, which provides a convolutional layer-based deep learning model by imaging the asymmetry of EEG, is an effective method for measuring the severity of depression. In addition, there are various advantages of using a model based on a convolution network. In the field of deep learning, which is a black box model, an additional explanation of why the model made this judgment is very important. So we present several studies generating saliency maps to make the model more interpretable in future work. In particular, in the CNN-based classifier problem, class activation mapping (CAM) [62] and gradient-weighted class activation mapping (Grad-CAM) [63] which suggest which part of the image led the CNN to the final classification decision, were used. Similarly, regression activation mapping [64], which can explain the regression results in regression problems, has been proposed. Applying such a method in future studies will help to create a more explanatory model with deep solution. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Dataset** & **Method** & **RMSE** \\ \hline \multirow{3}{*}{[32]} & AVEC2013 & Deep learning model based on & 9.82 \\ \cline{2-4}  & AVEC2014 & joint-tuning layers & 9.55 \\ \hline \multirow{3}{*}{[33]} & AVEC2013 & Deep learning model based on & 8.25 \\ \cline{2-4}  & AVEC2014 & expectation loss & 8.23 \\ \hline \multirow{3}{*}{[61]} & \multirow{3}{*}{EEG dataset} & A machine learning model based & \multirow{3}{*}{7.69} \\  & & on functional connectivity & \\ \cline{1-1}  & & feature extraction in EEG signals & \\ \hline \multirow{3}{*}{Proposed} & \multirow{3}{*}{EEG dataset [35]} & Deep learning model based on & \multirow{3}{*}{3.46} \\ \cline{1-1}  & & convolutional network using a & \\ \cline{1-1}  & & deep-asymmetry image & \\ \hline \hline \end{tabular}
\end{table}