# Prediction of Beck Depression Inventory Score in EEG: Application of Deep-Asymmetry Method
###### Abstract  
There is ongoing research on using electroencephalography (EEG) to predict depression. In particular, the deep learning method in which brain waves are used as inputs of a convolutional neural network (CNN) is being widely researched and has shown remarkable performance. We built a regression model to predict the severity score (Beck Depression Inventory [BDI]) of depressed patients as an extension of the deep-asymmetry method, which has shown promising performance in depression classification. Predicting the severity of depression is very important because the treatment and coping methods are different for each severity level. We imaged brain waves using the deep-asymmetry method, used them to train a two-dimensional CNN-based deep learning model, and achieved satisfactory performance. The EEG image-based CNN approach will make an important contribution to creating a highly interpretable model for predicting depression in the future. convolutional neural network; Beck depression inventory; electroencephalography; deep-asymmetry; regression model +
Footnote †: journal: Medical  
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In light of this, a CDSS based on objective data is one of the most appropriate solutions for diagnosing depression and brain diseases. Traditionally, brain diseases are diagnosed through medical data-based research using methods such as computed tomography (CT) [9], magnetic resonance imaging (MRI) [10], and functional magnetic resonance imaging (fMRI) [11]. Among them, electroencephalography (EEG) is being widely researched because it is relatively low-cost and easy to implement compared with the methods described above. In addition to depression, many brain diseases have been studied using EEG analysis. Studies have been conducted to predict dementia [12], schizophrenia [13], epilepsy [14], etc., using EEG. In particular, various attempts have been made to identify biomarkers of depression through EEG analysis [15]. Decreased frontal lobe activity in EEG [16] and alpha-hemispheric asymmetry [17] have been identified as representative biomarkers of depressive EEG. In these studies, incorporating machine learning techniques has led to significant progress in EEG-based depression prediction. The machine learning technique enables the development of effective models for classifying depressed patients and healthy controls using various features extracted from brain waves. [PERSON] presented a machine learning model to classify MDD patients and healthy controls using different EEG frequency bands and EEG alpha hemisphere asymmetry as derived features [18]. Similarly, [PERSON] et al. constructed variables by separately characterizing the alpha 1 and alpha 2 bands based on band power, asymmetry characteristics, and alpha band power and built a machine learning model based on this characterization [19]. In addition, [PERSON] et al. presented a machine learning classification model that uses both linear characteristics such as band power and hemispheric asymmetry characteristics and nonlinear characteristics such as relative wavelet energy and wavelet entropy [20]. In addition, deep learning techniques have been applied to diagnose patients with depression. Machine learning techniques entail detailed feature extraction and feature-selection techniques. Deep learning techniques have been used extensively in the field of medical diagnosis as they have the ability to learn important characteristics by learning automatically based on raw data. It is particularly noteworthy that many deep learning model architectures use convolutional neural network (CNN)-based layers. There are two main approaches to applying CNNs in the field of EEG. First, there is a method of providing raw EEG data to a CNN in the form of time series data. For example, [PERSON] et al. presented a model to classify depressed patients and healthy controls using EEG signals and a 13-layer deep CNN model [21]. [PERSON] et al.