Depression Assessment Method: An EEG Emotion Recognition Framework Based on Spatiotemporal Neural Network Article Swipe
Hongli Chang
,
Yuan Zong
,
Wenming Zheng
,
Chuangao Tang
,
Jie Zhu
,
C. Shan Xu
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.3389/fpsyt.2021.837149
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/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.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3389/fpsyt.2021.837149
- https://www.frontiersin.org/articles/10.3389/fpsyt.2021.837149/pdf
- OA Status
- gold
- Cited By
- 43
- References
- 68
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4220656408
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- OpenAlex ID
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https://openalex.org/W4220656408Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3389/fpsyt.2021.837149Digital Object Identifier
- Title
-
Depression Assessment Method: An EEG Emotion Recognition Framework Based on Spatiotemporal Neural NetworkWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2022Year of publication
- Publication date
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2022-03-15Full publication date if available
- Authors
-
Hongli Chang, Yuan Zong, Wenming Zheng, Chuangao Tang, Jie Zhu, C. Shan XuList of authors in order
- Landing page
-
https://doi.org/10.3389/fpsyt.2021.837149Publisher landing page
- PDF URL
-
https://www.frontiersin.org/articles/10.3389/fpsyt.2021.837149/pdfDirect link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
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https://www.frontiersin.org/articles/10.3389/fpsyt.2021.837149/pdfDirect OA link when available
- Concepts
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Electroencephalography, Computer science, Feature extraction, Pattern recognition (psychology), Preprocessor, Artificial intelligence, Convolutional neural network, Feature (linguistics), Emotion classification, Speech recognition, Psychology, Neuroscience, Linguistics, PhilosophyTop concepts (fields/topics) attached by OpenAlex
- Cited by
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43Total citation count in OpenAlex
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2025: 12, 2024: 16, 2023: 14, 2022: 1Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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