# Prediction of Beck Depression Inventory Score in EEG: Application of Deep-Asymmetry Method
## 2 Materials and Methods
### BDI Regression Model  
We built the most general baseline model to compare and validate regression models predicting depression severity scores using the deep-asymmetry visualization method. This method is a signal-based approach for EEG. Each EEG that had undergone data preprocessing was used as the input for the baseline model without any feature extraction. The baseline model uses one-dimensional convolutional layers, max pooling layers, and fully connected layers. We used a ReLU activation function [42] for each convolutional layer in the models. This method is commonly used in conventional brain-wave deep-learning analysis. Table 1 provides the detailed parameters of the signal-based baseline model.  
For a close comparison, we built an image-based baseline model using the most common EEG imaging methodology. We imaged the EEG using the STFT Spectrogram method, which was used in the existing methodology [23; 24; 26; 28]. Figure 6 is an example of a spectrogram image to be used as an input of an image-based baseline model. In conducting STFT, the fast Fourier transform (FFT) points are 1024; the hop_length is 256; the window size is 1024 (2 s), and the sampling rate is 512 Hz.  
As in the method used by [PERSON] et al. [26], we constructed a single image by arranging the spectral images by channel. Similarly, we built a CNN model for BDI score regression based on deep learning. Table 2 presents the detailed parameters of the proposed image-based baseline model.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Layer**} &
\begin{tabular}{c} **Kernel Size** \\ **(Pool Size)** \\ \end{tabular} & \multirow{2}{*}{**Activation**} & \multirow{2}{*}{**Output Shape**} & \multirow{2}{*}{**Param \#**} \\ \hline Convolution 1D & 4 & ReLU & 5117, 64 & 4928 \\ MaxPooling 1D & 2 & & 2558, 64 & 0 \\ Convolution 1D & 2 & ReLU & 2557, 32 & 4128 \\ MaxPooling 1D & 2 & & 1278, 32 & 0 \\ Convolution 1D & 2 & ReLU & 1277, 16 & 1040 \\ MaxPooling 1D & 2 & & 638, 16 & 0 \\ Flatten & & & 10,208 & 0 \\ Dense & & ReLU & 64 & 653,376 \\ Dense & & linear & 1 & 65 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters of the signal-based baseline model.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Layer**} &
\begin{tabular}{c} **Kernel Size** \\ **(Pool Size)** \\ \end{tabular} & \multirow{2}{*}{**Activation**} & \multirow{2}{*}{**Output Shape**} & \multirow{2}{*}{**Param \#**} \\ \hline Convolution 2D & 2 \(\times\) 2 & ReLU & 683, 28, 16 & 208 \\ MaxPooling 2D & 2 \(\times\) 2 & & 341, 14, 16 & 0 \\ Batch Normalization & & & 341, 14, 16 & 64 \\ Convolution 2D & 2 \(\times\) 2 & ReLU & 340, 13, 16 & 520 \\ MaxPooling 2D & 2 \(\times\) 2 & & 170, 6, 8 & 0 \\ Convolution 2D & 2 \(\times\) 2 & ReLU & 168, 4, 4 & 292 \\ MaxPooling 2D & 2 \(\times\) 2 & & 84, 2, 4 & 0 \\ Flatten & & & 672 & 0 \\ Dense & & ReLU & 256 & 172,288 \\ Dropout (0.25) & & & 256 & 0 \\ Dense & & linear & 1 & 257 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Parameters of the image-based baseline model.  
Figure 6: Example of STFT spectrogram image. STFT spectrogram images for each channel ‘FP1’, ‘FP2’, ‘F7’, ‘F3’, ‘FZ’, ‘F4’, ‘F8’, ‘T7’, ‘C3’, ‘CZ’, ‘C4’’, ‘T8’, ‘P7’, ‘P3’, ‘PZ’, ‘P4’, ‘P8’, ‘O1’, ‘O2’ were merged in that order.  
In addition, we present a deep learning regression model that applies the proposed deep-asymmetry visualization method. Table 3 shows the parameters of the model used, and Figure 7 shows a schematic of the architecture of the model. To predict BDI scores based on asymmetric matrix images, we used a model consisting of a two-dimensional CNN, a pooling layer, and a fully connected layer. However, unlike previous studies that used deep learning models for classification, we used the activation function of the last dense layer as a linear function to solve the regression problem. A batch normalization layer [43] and dropout layer [44] were used to reduce overfitting during model training.