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Depression Assessment Method: An EEG Emotion Recognition Framework Based on Spatiotemporal Neural Network
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, ele...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8967371/ https://www.ncbi.nlm.nih.gov/pubmed/35368726 http://dx.doi.org/10.3389/fpsyt.2021.837149 |
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author | Chang, Hongli Zong, Yuan Zheng, Wenming Tang, Chuangao Zhu, Jie Li, Xuejun |
author_facet | Chang, Hongli Zong, Yuan Zheng, Wenming Tang, Chuangao Zhu, Jie Li, Xuejun |
author_sort | Chang, Hongli |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-8967371 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-89673712022-03-31 Depression Assessment Method: An EEG Emotion Recognition Framework Based on Spatiotemporal Neural Network Chang, Hongli Zong, Yuan Zheng, Wenming Tang, Chuangao Zhu, Jie Li, Xuejun Front Psychiatry Psychiatry 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. Frontiers Media S.A. 2022-03-15 /pmc/articles/PMC8967371/ /pubmed/35368726 http://dx.doi.org/10.3389/fpsyt.2021.837149 Text en Copyright © 2022 Chang, Zong, Zheng, Tang, Zhu and Li. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Psychiatry Chang, Hongli Zong, Yuan Zheng, Wenming Tang, Chuangao Zhu, Jie Li, Xuejun Depression Assessment Method: An EEG Emotion Recognition Framework Based on Spatiotemporal Neural Network |
title | Depression Assessment Method: An EEG Emotion Recognition Framework Based on Spatiotemporal Neural Network |
title_full | Depression Assessment Method: An EEG Emotion Recognition Framework Based on Spatiotemporal Neural Network |
title_fullStr | Depression Assessment Method: An EEG Emotion Recognition Framework Based on Spatiotemporal Neural Network |
title_full_unstemmed | Depression Assessment Method: An EEG Emotion Recognition Framework Based on Spatiotemporal Neural Network |
title_short | Depression Assessment Method: An EEG Emotion Recognition Framework Based on Spatiotemporal Neural Network |
title_sort | depression assessment method: an eeg emotion recognition framework based on spatiotemporal neural network |
topic | Psychiatry |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8967371/ https://www.ncbi.nlm.nih.gov/pubmed/35368726 http://dx.doi.org/10.3389/fpsyt.2021.837149 |
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