SparNet: A Convolutional Neural Network for EEG Space-Frequency Feature Learning and Depression Discrimination

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 dee...

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Bibliographic Details
Published in:Frontiers in neuroinformatics 2022-06, Vol.16, p.914823-914823
Main Authors: Deng, Xin, Fan, Xufeng, Lv, Xiangwei, Sun, Kaiwei
Format: Article
Language:English
Subjects:
Accuracy
Brain research
Classification
Datasets
Decomposition
Deep learning
depression
EEG
Electrodes
Electroencephalography
Filters
Frequency dependence
Mental depression
Mental disorders
Methods
Neural networks
Neuroscience
Physiology
SENet
space-frequency domain characteristics
SparNet
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Summary: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 researchers 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 the cross-validation method of subject division. The results show that SparNet achieves a sensitivity 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.
ISSN:1662-5196
1662-5196
DOI:10.3389/fninf.2022.914823