BMFCNet: Blended Multilevel Features With Constraint Fusion Network for Depression Detection From EEG Signals

Depression is a psychological illness characterized by the recurrent occurrence of a negative emotional state. The common cause of the rise in suicide cases that appear globally is major depressive disorder (MDD). Therefore, proper diagnosis and therapy are necessary to lessen the impact of depression. MDD is a serious and common ailment that causes functional frailty. However, its exact symptoms remain unknown. Therefore, the task of manually detecting MDD is challenging and subjective. Although electroencephalogram (EEG) signals have demonstrated potential in diagnosis, we still need to enhance their accuracy, clinical value, and efficiency. This study employs a unique method known as Blended Multilevel Features with Constraint Fusion Network (BMFCNet) to identify MDD. The residual-inception (RI) module of the BMFCNet extracts the most pertinent and discriminative characteristics from the high-level (HL) and low-level (LL) features. To combine improved LL and HL traits, a constraint fusion technique is introduced to weigh and fuse the LL and HL features adaptively. Furthermore, we demonstrate the effectiveness of our method by analogizing it with sixteen state-of-the-art (SOTA) methodologies utilizing two benchmark datasets.