Abstract
The brain-computer interface (BCI) has become one of the most important biomedical research fields and has created many useful applications. As an important component of BCI, electroencephalography (EEG) is in general sensitive to noise and rich in all kinds of information from our brain. In this paper, we introduce a new strategy to filter out unwanted features from EEG signals using GAN-based autoencoders. Filtering out signals relating to one property of the EEG signal while retaining another is similar to the way we can listen to just one voice during a party. This approach has many potential applications including in privacy and security. We use the UCI EEG dataset on alcoholism for our experiments. Our experiment results show that our novel GAN based structure can filter out alcoholism information for 66% of EEG signals with an average of only 6.2% accuracy lost.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Kumari, P., Vaish, A.: Brainwave based user identification system: a pilot study in robotics environment. Robot. Auton. Syst. 65, 15–23 (2015)
Truong, N.D., Nguyen, A.D., Kuhlmann, L., Bonyadi, M.R., Yang, J., Kavehei, O.: A Generalised Seizure Prediction with Convolutional Neural Networks for Intracranial and Scalp Electroencephalogram Data Analysis. arXiv preprint arXiv:1707.01976 (2017)
Thodoroff, P., Pineau, J., Lim, A.: Learning robust features using deep learning for automatic seizure detection. In: Machine Learning for Healthcare Conference, pp. 178–190 (2016)
Ebrahimi, F., Mikaeili, M., Estrada, E., Nazeran, H.: Automatic sleep stage classification based on EEG signals by using neural networks and wavelet packet coefficients. In: 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2008. EMBS 2008, pp. 1151–1154. IEEE (2008)
Palazzo, S., Spampinato, C., Kavasidis, I., Giordano, D., Shah, M.: Generative adversarial networks conditioned by brain signals. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3410–3418 (2017)
Yao, Y., Plested, J., Gedeon, T.: Deep Feature Learning and Visualization for EEG Recording Using Autoencoders. Submited to International Conference on Neural Information Processing (ICONIP) 2018 12 (2018)
Isola, P., Zhu, J.-Y., Zhou, T., Efros, A.A.: Image-to-image translation with conditional adversarial networks. arXiv preprint arXiv:1611.07004 (2017)
Bashivan, P., Rish, I., Yeasin, M., Codella, N.: Learning representations from EEG with deep recurrent-convolutional neural networks. arXiv preprint arXiv:1511.06448 (2015)
Goodfellow, I., et al.: Generative adversarial nets. In: Advances in Neural Information Processing Systems, pp. 2672–2680 (2014)
Radford, A., Metz, L., Chintala, S.: Unsupervised representation learning with deep convolutional generative adversarial networks. arXiv preprint arXiv:1511.06434 (2015)
Liu, M.-Y., Breuel, T., Kautz, J.: Unsupervised image-to-image translation networks. In: Advances in Neural Information Processing Systems, pp. 700–708 (2017)
Arjovsky, M., Chintala, S., Bottou, L.: Wasserstein gan. arXiv preprint arXiv:1701.07875 (2017)
Zhu, J.-Y., Park, T., Isola, P., Efros, A.A.: Unpaired image-to-image translation using cycle-consistent adversarial networks. arXiv preprint arXiv:1703.10593 (2017)
LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proc. IEEE 86(11), 2278–2324 (1998). Wiley-IEEE Press, Indianapolis, Indiana
Krizhevsky, A., Hinton, G.: Learning multiple layers of features from tiny images (2009)
Yu, L., Zhang, W., Wang, J., Yu, Y.: SeqGAN: Sequence Generative Adversarial Nets with Policy Gradient. arXiv preprint arXiv:1609.05473 (2016)
Guo, J., Lu, S., Cai, H., Zhang, W., Yu, Y., Wang, J.: Long Text Generation via Adversarial Training with Leaked Information. arXiv preprint arXiv:1709.08624 (2017)
Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234–241. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24574-4_28
Lawhern, V.J., Solon, A.J., Waytowich, N.R., Gordon, S.M., Hung, C.P., Lance, B.J.: Eegnet: a compact convolutional network for eeg-based brain-computer interfaces. arXiv preprint arXiv:1611.08024 (2016)
Li, Y., Dzirasa, K., Carin, L., Carlson, D.E.: Targeting EEG/LFP synchrony with neural nets. In: Advances in Neural Information Processing Systems, pp. 4623–4633 (2017)
Lucic, M., Kurach, K., Michalski, M., Gelly, S., Bousquet, O.: Are gans created equal? a large-scale study. arXiv preprint arXiv:1711.10337 (2017)
Goodfellow, I.J., Shlens, J., Szegedy, C.: Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 (2014)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Switzerland AG
About this paper
Cite this paper
Yao, Y., Plested, J., Gedeon, T. (2018). A Feature Filter for EEG Using Cycle-GAN Structure. In: Cheng, L., Leung, A., Ozawa, S. (eds) Neural Information Processing. ICONIP 2018. Lecture Notes in Computer Science(), vol 11307. Springer, Cham. https://doi.org/10.1007/978-3-030-04239-4_51
Download citation
DOI: https://doi.org/10.1007/978-3-030-04239-4_51
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-04238-7
Online ISBN: 978-3-030-04239-4
eBook Packages: Computer ScienceComputer Science (R0)