Depressive Disorder Recognition Based on Frontal EEG Signals and Deep Learning
Depressive disorder (DD) has become one of the most common mental diseases, seriously endangering both the affected person’s psychological and physical health. Nowadays, a DD diagnosis mainly relies on the experience of clinical psychiatrists and subjective scales, lacking objective, accurate, pract...
Main Authors: | Yanting Xu, Hongyang Zhong, Shangyan Ying, Wei Liu, Guibin Chen, Xiaodong Luo, Gang Li |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2023-10-01
|
Series: | Sensors |
Subjects: | |
Online Access: | https://www.mdpi.com/1424-8220/23/20/8639 |
Similar Items
-
Depression Assessment Method: An EEG Emotion Recognition Framework Based on Spatiotemporal Neural Network
by: Hongli Chang, et al.
Published: (2022-03-01) -
A Multi-Channel Feature Fusion CNN-Bi-LSTM Epilepsy EEG Classification and Prediction Model Based on Attention Mechanism
by: Yahong Ma, et al.
Published: (2023-01-01) -
CDBA: a novel multi-branch feature fusion model for EEG-based emotion recognition
by: Zhentao Huang, et al.
Published: (2023-07-01) -
Neuroimaging Study of Brain Functional Differences in Generalized Anxiety Disorder and Depressive Disorder
by: Xuchen Qi, et al.
Published: (2023-09-01) -
Automatic Detection of Abnormal EEG Signals Using WaveNet and LSTM
by: Hezam Albaqami, et al.
Published: (2023-06-01)