Component-mixing strategy: A decomposition-based data augmentation algorithm for motor imagery signals
Deep learning has achieved a remarkable success in areas such as brain-computer interface systems (BCI). However, electroencephalography (EEG) signals evoked by motor imagery (MI) are sometimes limited in their amount due to invalid data caused by the subjects’ fatigue, leading to a performance degr...
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| Published in | Neurocomputing (Amsterdam) Vol. 465; pp. 325 - 335 |
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| Main Authors | , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Elsevier B.V
20.11.2021
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| Subjects | |
| Online Access | Get full text |
| ISSN | 0925-2312 1872-8286 |
| DOI | 10.1016/j.neucom.2021.08.119 |
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| Abstract | Deep learning has achieved a remarkable success in areas such as brain-computer interface systems (BCI). However, electroencephalography (EEG) signals evoked by motor imagery (MI) are sometimes limited in their amount due to invalid data caused by the subjects’ fatigue, leading to a performance degradation. To this end, in this work we extend empirical mode decomposition into multivariate empirical mode decomposition and intrinsic time-scale decomposition, proposing a component-mixing strategy (CMS) for MI data augmentation. Compared to commonly used data augmentation methods such as generative adversarial networks, CMS can generate artificial trials from a few training samples without any required training. We claim that raw and artificial data generated by CMS are consistent with respect to the distribution and power spectral density. Experiments done on the BCI Competition IV dataset 2b show that CMS can achieve a considerable improvement on the binary classification accuracy and the area under the curve score using EEGNet, wavelet neural networks and a support vector machine. |
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| AbstractList | Deep learning has achieved a remarkable success in areas such as brain-computer interface systems (BCI). However, electroencephalography (EEG) signals evoked by motor imagery (MI) are sometimes limited in their amount due to invalid data caused by the subjects’ fatigue, leading to a performance degradation. To this end, in this work we extend empirical mode decomposition into multivariate empirical mode decomposition and intrinsic time-scale decomposition, proposing a component-mixing strategy (CMS) for MI data augmentation. Compared to commonly used data augmentation methods such as generative adversarial networks, CMS can generate artificial trials from a few training samples without any required training. We claim that raw and artificial data generated by CMS are consistent with respect to the distribution and power spectral density. Experiments done on the BCI Competition IV dataset 2b show that CMS can achieve a considerable improvement on the binary classification accuracy and the area under the curve score using EEGNet, wavelet neural networks and a support vector machine. |
| Author | Li, Binghua Zhang, Zhiwen Solé-Casals, Jordi Yang, Zhenglu Duan, Feng Sun, Zhe Zhao, Qibin |
| Author_xml | – sequence: 1 givenname: Binghua surname: Li fullname: Li, Binghua organization: College of Artificial Intelligence, Nankai University, Tianjin 300071, China – sequence: 2 givenname: Zhiwen surname: Zhang fullname: Zhang, Zhiwen organization: College of Artificial Intelligence, Nankai University, Tianjin 300071, China – sequence: 3 givenname: Feng surname: Duan fullname: Duan, Feng email: duanf@nankai.edu.cn organization: College of Artificial Intelligence, Nankai University, Tianjin 300071, China – sequence: 4 givenname: Zhenglu surname: Yang fullname: Yang, Zhenglu organization: College of Computer Science, Nankai University, Tianjin 300071, China – sequence: 5 givenname: Qibin surname: Zhao fullname: Zhao, Qibin organization: Tensor Learning Team, RIKEN Center for Advanced Intelligence Project, Tokyo 103-0027, Japan – sequence: 6 givenname: Zhe surname: Sun fullname: Sun, Zhe email: zhe.sun.vk@riken.jp organization: Computational Engineering Applications Unit, Head Office for Information Systems and Cybersecurity, RIKEN, Saitama 351-0198, Japan – sequence: 7 givenname: Jordi surname: Solé-Casals fullname: Solé-Casals, Jordi email: jordi.sole@uvic.cat organization: College of Artificial Intelligence, Nankai University, Tianjin 300071, China |
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| Title | Component-mixing strategy: A decomposition-based data augmentation algorithm for motor imagery signals |
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