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

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) being widely researched and has shown remarkable performance. We built regression model severity score (Beck Depression Inventory [BDI]) depressed patients an extension deep-asymmetry method, promising performance depression classification. Predicting very important because treatment coping methods different for each level. imaged them train two-dimensional CNN-based model, achieved satisfactory The EEG image-based CNN approach will make contribution creating highly interpretable predicting future.