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

• Published in: Frontiers in human neuroscience Vol. 14; p. 284
• Main Authors: Duan, Lijuan, Duan, Huifeng, Qiao, Yuanhua, Sha, Sha, Qi, Shunai, Zhang, Xiaolong, Huang, Juan, Huang, Xiaohan, Wang, Changming
• Format: Journal Article
• Language: English
• Published: Lausanne Frontiers Research Foundation 23.09.2020; Frontiers Media S.A
• Subjects: Accuracy; Artificial intelligence; Asymmetry; Brain; Cerebral hemispheres; Classification; cross correlation; EEG; Electroconvulsive therapy; Electrodes; Electroencephalography; feature; Human Neuroscience; interhemispheric asymmetry; Learning algorithms; Machine learning; major depressive disorder (MDD); Medical research; Mental depression; Mental disorders; Neural networks; Time series; Wavelet transforms; Beijing China

Emotional decoding and automatic identification of major depressive disorder (MDD) is helpful to doctors in diagnosis of the disease on time, and electroencephalogram (EEG) is sensitive to the changes of functional state of human brain, showing its potential to help to diagnose MDD. In this paper, a...