机构：[1]Faculty of Information Technology, Beijing University of Technology, Beijing, China,[2]Beijing Key Laboratory of Trusted Computing, Beijing, China,[3]National Engineering Laboratory for Critical Technologies of Information Security Classified Protection, Beijing, China,[4]College of Applied Sciences, Beijing University of Technology, Beijing, China,[5]Beijing Anding Hospital, Capital Medical University, Beijing, China,[6]Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China,神经疾病高创中心（北京学者工作室）[7]Brain-inspired Intelligence and Clinical Translational Research Center, Xuanwu Hospital, Capitap Medical University, Beijing, China,[8]Department of Neurosurgery, Xuanwu Hospital, Capitap Medical University, Beijing, China神经科系统神经外科
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)]. First, the structural features and connectivity features of the theta-, alpha-, and beta-frequency bands are extracted on the preprocessed and segmented EEG signals. Second, the structural feature matrix of the theta-, alpha-, and beta-frequency bands are added to and subtracted from the connectivity feature matrix to obtain mixed features. Finally, the structural features, connectivity features, and the mixed features are fed to three classifiers to select suitable features for the classification, and it is found that our mode achieves the best classification results using the mixed features. The results are also compared with those from some state-of-the-art methods, and we achieved an accuracy of 94.13%, a sensitivity of 95.74%, a specificity of 93.52%, and an F1-score (f1) of 95.62% on the data from Beijing Anding Hospital, Capital Medical University. The study could be generalized to develop a system that may be helpful in clinical purposes.
第一作者机构：[1]Faculty of Information Technology, Beijing University of Technology, Beijing, China,[2]Beijing Key Laboratory of Trusted Computing, Beijing, China,[3]National Engineering Laboratory for Critical Technologies of Information Security Classified Protection, Beijing, China,
共同第一作者：
通讯作者：
通讯机构：[5]Beijing Anding Hospital, Capital Medical University, Beijing, China,[6]Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China,[7]Brain-inspired Intelligence and Clinical Translational Research Center, Xuanwu Hospital, Capitap Medical University, Beijing, China,[8]Department of Neurosurgery, Xuanwu Hospital, Capitap Medical University, Beijing, China
推荐引用方式(GB/T 7714)：
Duan Lijuan,Duan Huifeng,Qiao Yuanhua,et al.Machine Learning Approaches for MDD Detection and Emotion Decoding Using EEG Signals[J].FRONTIERS IN HUMAN NEUROSCIENCE.2020,14:doi:10.3389/fnhum.2020.00284.
APA：
Duan, Lijuan,Duan, Huifeng,Qiao, Yuanhua,Sha, Sha,Qi, Shunai...&Sha, Sha.(2020).Machine Learning Approaches for MDD Detection and Emotion Decoding Using EEG Signals.FRONTIERS IN HUMAN NEUROSCIENCE,14,
MLA：
Duan, Lijuan,et al."Machine Learning Approaches for MDD Detection and Emotion Decoding Using EEG Signals".FRONTIERS IN HUMAN NEUROSCIENCE 14.(2020)
