Major Depressive Disorder (MDD) - can be evaluated by advanced neurocomputing
and traditional machine learning techniques. This study aims to develop an
automatic system based on a Brain-Computer Interface (BCI) to classify and score
depressive patients by specific frequency bands and electrodes. In this study,
two Residual Neural Networks (ResNets) based on electroencephalogram (EEG)
monitoring are presented for classifying depression (classifier) and for scoring
depressive severity (regression). Significant frequency bands and specific brain
regions are selected to improve the performance of the ResNets. The algorithm,
which is estimated by 10-fold cross-validation, attained an average accuracy
rate ranging from 0.371 to 0.571 and achieved average Root-Mean-Square Error
(RMSE) from 7.25 to 8.41. After using the beta frequency band and 16 specific
EEG channels, we obtained the best-classifying accuracy at 0.871 and the
smallest RMSE at 2.80. It was discovered that signals extracted from the beta
band are more distinctive in depression classification, and these selected
channels tend to perform better on scoring depressive severity. Our study also
uncovered the different brain architectural connections by relying on phase
coherence analysis. Increased delta deactivation accompanied by strong beta
activation is the main feature of depression when the depression symptom is
becoming more severe. We can therefore conclude that the model developed here is
acceptable for classifying depression and for scoring depressive severity. Our
model can offer physicians a model that consists of topological dependency,
quantified semantic depressive symptoms and clinical features by using EEG
signals. These selected brain regions and significant beta frequency bands can
improve the performance of the BCI system for detecting depression and scoring
depressive severity.