Depression Assessment Method: An EEG Emotion Recognition Framework Based on Spatiotemporal Neural Network

• Published in: Frontiers in psychiatry Vol. 12; p. 837149
• Main Authors: Chang, Hongli, Zong, Yuan, Zheng, Wenming, Tang, Chuangao, Zhu, Jie, Li, Xuejun
• Format: Journal Article
• Language: English
• Published: Switzerland Frontiers Media S.A 15.03.2022
• Subjects: convolutional neural network (CNN); depression; electroencephalogram (EEG); emotion recognition; long-short term memory network (LSTM); Psychiatry; long-short term memory network (LSTM); depression; convolutional neural network (CNN); emotion recognition; electroencephalogram (EEG)
• ISSN: 1664-0640; 1664-0640
• DOI: 10.3389/fpsyt.2021.837149

The main characteristic of depression is emotional dysfunction, manifested by increased levels of negative emotions and decreased levels of positive emotions. Therefore, accurate emotion recognition is an effective way to assess depression. Among the various signals used for emotion recognition, electroencephalogram (EEG) signal has attracted widespread attention due to its multiple advantages, such as rich spatiotemporal information in multi-channel EEG signals. First, we use filtering and Euclidean alignment for data preprocessing. In the feature extraction, we use short-time Fourier transform and Hilbert–Huang transform to extract time-frequency features, and convolutional neural networks to extract spatial features. Finally, bi-directional long short-term memory explored the timing relationship. Before performing the convolution operation, according to the unique topology of the EEG channel, the EEG features are converted into 3D tensors. This study has achieved good results on two emotion databases: SEED and Emotional BCI of 2020 WORLD ROBOT COMPETITION. We applied this method to the recognition of depression based on EEG and achieved a recognition rate of more than 70% under the five-fold cross-validation. In addition, the subject-independent protocol on SEED data has achieved a state-of-the-art recognition rate, which exceeds the existing research methods. We propose a novel EEG emotion recognition framework for depression detection, which provides a robust algorithm for real-time clinical depression detection based on EEG.