SparNet: A Convolutional Neural Network for EEG Space-Frequency Feature Learning and Depression Discrimination - Deng, Xin; Fan, Xufeng; Lv, Xiangwei; Sun, Kaiwei (Frontiers in Neuroinformatics, 2022)
Depression affects many people around the world today and is considered a global problem. Electroencephalogram (EEG) measurement is an appropriate way to understand the underlying mechanisms of major depressive disorder (MDD) to distinguish depression from normal control. With the development of deep learning methods, many researches have adopted deep learning models to improve the classification accuracy of depression recognition. However, there are few studies on designing convolution filters for spatial and frequency domain feature learning in different brain regions. In this study, SparNet, a convolutional neural network composed of five parallel convolutional filters and the SENet, is proposed to learn EEG space-frequency domain characteristics and distinguish between depressive and normal control. The model is trained and tested by cross validation method of subject division. The results show that SparNet achieves a sensitive of 95.07$%, a specificity of 93.66$%, and an accuracy of 94.37$% in classification. Therefore, our results can conclude that the proposed SparNet model is effective in detecting depression using EEG signals. It also indicates that the combination of spatial information and frequency domain information is an effective way to identify patients with depression.