Improving EEG-based motor imagery classification with conditional Wasserstein GAN

Zheng Li; Yang Yu

doi:10.1117/12.2581328

10 November 2020 Improving EEG-based motor imagery classification with conditional Wasserstein GAN

Zheng Li, Yang Yu

Proceedings Volume 11584, 2020 International Conference on Image, Video Processing and Artificial Intelligence; 115841U (2020) https://doi.org/10.1117/12.2581328
Event: Third International Conference on Image, Video Processing and Artificial Intelligence, 2020, Shanghai, China

Abstract

Deep learning based algorithms have made huge progress in the field of image classification and speech recognition. There is an increasing number of researchers beginning to use deep learning to process electroencephalographic(EEG) brain signals. However, at the same time, due to the complexity of the experimental device and the expensive collection cost, we cannot train a powerful deep learning model without enough satisfactory EEG data. Data augmentation has been considered as an effective method to eliminate this issue. We propose the Conditional Wasserstein Generative Adversarial Network with gradient penalty (CWGAN-GP) to synthesize EEG data for data augmentation. We use two public neural networks for a motor imagery task and combine the synthesized data with real EEG data to test the generated samples’ data enhancement effect. The results indicate that our model can generate high-quality artificial EEG data, which can effectively learn the features from the original EEG data. Both neural networks have gained improved classification performance, and the more complex one has obtained more significant performance improvement. The experiment provides us with new ideas for improving the performance of EEG signal processing.

Citation Download Citation

Zheng Li and Yang Yu "Improving EEG-based motor imagery classification with conditional Wasserstein GAN", Proc. SPIE 11584, 2020 International Conference on Image, Video Processing and Artificial Intelligence, 115841U (10 November 2020); https://doi.org/10.1117/12.2581328

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KEYWORDS

Electroencephalography

Data modeling

Gallium nitride

Convolution

Neural networks

Statistical modeling

Signal processing

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