Abstract
Early diagnosis of depression is very important. In particular, predicting the severity of depression is very important as it can provide appropriate treatment for people with depression. We propose a deep learning model that predicts the Beck's Depression Inventory (BDI) score, which is one of the indicators of the severity of depression, using electroencephalogram (EEG), which is objective physiological data. As a result, the proposed method uses the EEG shows that you can effectively predict the BDI scores.
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