4-Class MI-EEG Signal Generation and Recognition with CVAE-GAN
| Autor: | Zhuangfei Chen, Tao Shen, Huijuan Yu, Jun Yang, Yaolian Song |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Discriminator
Computer science Interface (computing) 02 engineering and technology brain computer interface lcsh:Technology lcsh:Chemistry 03 medical and health sciences 0302 clinical medicine conditional variational auto-encoders Classifier (linguistics) 0202 electrical engineering electronic engineering information engineering General Materials Science lcsh:QH301-705.5 Instrumentation Brain–computer interface Fluid Flow and Transfer Processes Signal processing lcsh:T business.industry Process Chemistry and Technology Deep learning generative adversarial network General Engineering Pattern recognition lcsh:QC1-999 Computer Science Applications Generative model ComputingMethodologies_PATTERNRECOGNITION lcsh:Biology (General) lcsh:QD1-999 lcsh:TA1-2040 020201 artificial intelligence & image processing Artificial intelligence lcsh:Engineering (General). Civil engineering (General) business Encoder lcsh:Physics 030217 neurology & neurosurgery |
| Zdroj: | Applied Sciences Volume 11 Issue 4 Applied Sciences, Vol 11, Iss 1798, p 1798 (2021) |
| ISSN: | 2076-3417 |
| DOI: | 10.3390/app11041798 |
| Popis: | As the capability of an electroencephalogram’s (EEG) measurement of the real-time electrodynamics of the human brain is known to all, signal processing techniques, particularly deep learning, could either provide a novel solution for learning but also optimize robust representations from EEG signals. Considering the limited data collection and inadequate concentration of during subjects testing, it becomes essential to obtain sufficient training data and useful features with a potential end-user of a brain–computer interface (BCI) system. In this paper, we combined a conditional variational auto-encoder network (CVAE) with a generative adversarial network (GAN) for learning latent representations from EEG brain signals. By updating the fine-tuned parameter fed into the resulting generative model, we could synthetize the EEG signal under a specific category. We employed an encoder network to obtain the distributed samples of the EEG signal, and applied an adversarial learning mechanism to continuous optimization of the parameters of the generator, discriminator and classifier. The CVAE was adopted to adjust the synthetics more approximately to the real sample class. Finally, we demonstrated our approach take advantages of both statistic and feature matching to make the training process converge faster and more stable and address the problem of small-scale datasets in deep learning applications for motor imagery tasks through data augmentation. The augmented training datasets produced by our proposed CVAE-GAN method significantly enhance the performance of MI-EEG recognition. |
| Databáze: | OpenAIRE |
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