# Prediction of Beck Depression Inventory Score in EEG: Application of Deep-Asymmetry Method
###### Abstract
proposed the use of a one-dimensional CNN for raw EEG signals and a model combining a CNN and long short-term memory (LSTM) [22]. Another approach is to visualize important characteristics within the EEG and present it to the model in the form of an image. [PERSON] et al. obtained spectrograms using the short-time Fourier transform (STFT) to classify depressed patients and healthy controls and presented a model for pre-screening depressed patients using low channels [23]. [PERSON] et al. applied STFT-based prefrontal EEG images to VGG16, one of the latest deep learning architectures, to achieve high EEG classification performance [24]. [PERSON] et al. presented a deep learning approach for recognizing mild depression using a functional connectivity matrix and CNN [25]. [PERSON] et al. proposed applying transfer learning together with a feature vector and RGB image as CNN-based model inputs with the goal of recognizing mild depression [26]. [PERSON] et al. presented a deep learning classification model comprising a CNN and LSTM based on the effective connectivity between channels in EEGs [27]. For a decision support system for MDD detection, [PERSON] et al. achieved the highest level of classification performance with a deep learning method using an image converted from an STFT-based spectrogram and an 8-layer CNN [28]. In particular, the deep-asymmetry method [29] used in this study is a method for training a deep learning model based on a CNN by converting the alpha asymmetry, a representative biomarker of depression, into a matrix-type image. Consequently, patients and healthy controls were classified with high accuracy. However, the above-mentioned cases were limited to distinguishing between patients with depression and healthy controls. Depression is divided into several stages depending on its severity. The Beck Depression Inventory (BDI) [6] is one of the representative indicators of depression severity. The BDI consists of 21 sentences covering the cognitive, emotional, motivational, and physical symptoms of depression. It was first developed in 1961 and is widely used worldwide. It is scored by determining the total score of the responses to 21 sentences, and 0-13 is considered minimal depression, 14-19 mild depression, 20-28 moderate depression, and 29-63 severe depression. Furthermore, depression has different symptoms depending on its severity, and treatment and response methods are also different. Therefore, it is important to determine the severity of depression as well as to identify depression. We considered AVEC 2013 [30] and 2014 [31] as part of the related research. The purpose of this subtask was to predict BDI scores using the presented video and audio sets. Several studies using this dataset have been conducted. [PERSON] et al. presented a deep learning method based on a co-tuning layer that simultaneously captures facial appearance and dynamics, resulting in a MAE of 7.58 (AVEC2013) and a MAE of 7.47 (AVEC2014) [32]. [PERSON] et al. presented a deep learning architecture to predict depression levels through distribution learning and obtained MAEs of 6.30 (AVEC 2013) and 6.15 (AVEC2014) [33]. However, very few studies have presented a model predicting the severity of depression, such as the BDI, based on EEGs. We present a BDI regression model based on EEG as a follow-up study of the deep-asymmetry method validated by the existing depression patient classification model. We present a model for predicting the BDI by applying the deep asymmetry methodology, which has shown excellent performance in the classification of patients with MDD. EEGs are imaged using the asymmetry characteristic, a major biomarker of depression, and such images are used as inputs to a convolutional network. Models were tested on public datasets and compared with the most common baseline models. Figure 1 schematically illustrates the entire pipeline used in this study.