Fig. 1
From: A novel EEG-based major depressive disorder detection framework with two-stage feature selection
Proposed framework for MDD detection and severity prediction: Firstly, in the input module of the framework, the raw EEG signals are derived by Nerron-Spectrum-5 to obtain the EEG rhythm features, and the subjects are diagnosed and scored by a physician to get the HAMD-17 score. Then, in the data processing module, the \(\beta /\alpha\) features are extracted from the EEG rhythm features and are Z-score standardized together with the rhythm features to obtain standardized features. Subjects with HAMD-17 scores greater than 17 are labeled as MDD, and those with HAMD-17 scores less than or equal to 17 are labeled as non-MDD. Moreover, the HAMD-17 score directly served as an indicator of MDD severity assessment. Then, in the feature selection module, PCC carries out the first stage feature selection on standardized features, and RFE carries out the second stage feature selection on reserved features. Finally, LR and SVM are used as classification models to classify subjects into MDD and non-MDD. LNR is used as the regression model to assess the severity of MDD, and the HAMD-17 score predicted by LNR is used as the severity indicator of MDD