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Hashempour, S., Boostani, R., Mohammadi, M. and Sanei, S. 
ORCID: 0000-0002-3437-2801,
2022.
Continuous scoring of depression from EEG signals via a hybrid of convolutional neural networks.
IEEE Transactions on Neural Systems and Rehabilitation Engineering.
ISSN 1534-4320
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Abstract
Depression score is traditionally determined by taking the Beck depression inventory (BDI) test, which is a qualitative questionnaire. Quantitative scoring of depression has also been achieved by analyzing and classifying pre-recorded electroencephalography (EEG) signals. Here, we go one step further and apply raw EEG signals to a proposed hybrid convolutional and temporal-convolutional neural network (CNN-TCN) to continuously estimate the BDI score. In this research, the EEG signals of 119 individuals are captured by 64 scalp electrodes through successive eyes-closed and eyes-open intervals. Moreover, all the subjects take the BDI test and their scores are determined. The proposed CNN-TCN provides mean squared error (MSE) of 5.64±1.6 and mean absolute error (MAE) of 1.73±0.27 for eyes-open state and also provides MSE of 9.53±2.94 and MAE of 2.32±0.35 for the eyes-closed state, which significantly surpasses state-of-the-art deep network methods. In another approach, conventional EEG features are elicited from the EEG signals in successive frames and apply them to the proposed CNN-TCN in conjunction with known statistical regression methods. Our method provides MSE of 10.81±5.14 and MAE of 2.41±0.59 that statistically outperform the statistical regression methods. Moreover, the results with raw EEG are significantly better than those with EEG features.
| Item Type: | Journal article | ||||||
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| Publication Title: | IEEE Transactions on Neural Systems and Rehabilitation Engineering | ||||||
| Creators: | Hashempour, S., Boostani, R., Mohammadi, M. and Sanei, S. | ||||||
| Publisher: | Institute of Electrical and Electronics Engineers (IEEE) | ||||||
| Date: | 14 January 2022 | ||||||
| ISSN: | 1534-4320 | ||||||
| Identifiers: |
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| Rights: | © 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. | ||||||
| Divisions: | Schools > School of Science and Technology | ||||||
| Record created by: | Linda Sullivan | ||||||
| Date Added: | 19 Jan 2022 14:47 | ||||||
| Last Modified: | 19 Jan 2022 14:47 | ||||||
| URI: | https://irep.ntu.ac.uk/id/eprint/45366 |
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