EEG Depression Recognition Based on Multi-domain Features Combined with CBAM Model
At present, the electroencephalogram (EEG) identification method for depression mainly uses a single feature extraction method, which cannot cover multi-domain feature information, resulting in poor classification performance of the existing model. Therefore, this paper proposes a depression recogni...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | zho |
| Published: |
Harbin University of Science and Technology Publications
2024-06-01
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| Series: | Journal of Harbin University of Science and Technology |
| Subjects: | |
| Online Access: | https://hlgxb.hrbust.edu.cn/#/digest?ArticleID=2325 |
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| Summary: | At present, the electroencephalogram (EEG) identification method for depression mainly uses a single feature extraction method, which cannot cover multi-domain feature information, resulting in poor classification performance of the existing model. Therefore, this paper proposes a depression recognition algorithm based on multi-domain features combined with CBAM model (CNN- BiLSTM-Attention Mechanism). Firstly, the continuous wavelet transform (CWT) is used to extract time-frequency domain features, and combined with the spatial information of EEG electrodes to form a 2D feature image, which jointly retains the spatial, time and frequency information of EEG; then the convolutional neural network (CNN) is used) to extract spatial and frequency domain features, and then input bidirectional long and short-term memory ( BiLSTM) to capture time information; finally combined with attention mechanism (AM) , different weights are assigned to the multi-domain features extracted from the network, enabling the selection of more representative depressive features, thereby improving the accuracy of identifying depression. Experiments show that the depression recognition algorithm based on the CBAM model proposed in this paper has achieved an accuracy rate of 99. 10% on the public data set, which provides an effective new method for the research on depression recognition of EEG signals. |
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| ISSN: | 1007-2683 |