Machine Learning Approaches for MDD Detection and Emotion Decoding Using EEG Signals.

Emotional decoding and automatic identification of major depressive disorder (MDD) are helpful for the timely diagnosis of the disease. Electroencephalography (EEG) is sensitive to changes in the functional state of the human brain, showing its potential to help doctors diagnose MDD. In this paper, an approach for identifying MDD by fusing interhemispheric asymmetry and cross-correlation with EEG signals is proposed and tested on 32 subjects [16 patients with MDD and 16 healthy controls (HCs)].

Magnitude Squared Coherence Method based on Weighted Canonical Correlation Analysis for EEG Synchronization Analysis in Amnesic Mild Cognitive Impairment of Diabetes Mellitus.

Type 2 diabetes mellitus (T2DM) increases the risk of amnestic mild cognitive impairment (aMCI) and Alzheimer's disease (AD). aMCI is the transitory stage from normal cognition to AD, which seriously impacts the quality of human life, especially for old people. Electroencephalography (EEG) coherence can assess the functional connectivity between different brain regions, which is an effective way to research the pathogenesis of aMCI and distinguish aMCI patients from normal cognitive subjects.