Abstract
In this era of deep learning and big data, the transformation of biomedical big data into recognizable patterns is an important research focus and a great challenge in bioinformatics. An important form of biomedical data is electroencephalography (EEG) signals, which are generally strongly affected by noise and there exists notable individual, environmental and device differences. In this paper, we focus on learning discriminative features from short time EEG signals. Inspired by traditional image compression techniques to learn a robust representation of an image, we introduce and compare two strategies for learning features from EEG using two specifically designed autoencoders. Channel-wise autoencoders focus on features in each channel, while Image-wise autoencoders instead learn features from the whole trial. Our results on a UCI EEG dataset show that using both Channel-wise and Image-wise autoencoders achieve good performance for a classification problem with state of art accuracy in both within-subject and cross-subject tests. A further experiment using shared weights shows that the shared weights technique only slightly influenced learning but it reduced training time significantly.
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References
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 (2017). arXiv preprint: arXiv:1707.01976
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, 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)
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 (2016). arXiv preprint: arXiv:1611.08024
Min, S., Lee, B., Yoon, S.: Deep learning in bioinformatics. Brief. Bioinform. 18, 851–869 (2017)
Goodfellow, I., Bengio, Y., Courville, A., Bengio, Y.: Deep Learning. MIT Press, Cambridge (2016)
Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, pp. 1097–1105 (2012)
Hajinoroozi, M., Mao, Z., Jung, T.-P., Lin, C.-T., Huang, Y.: EEG-based prediction of driver’s cognitive performance by deep convolutional neural network. Signal Process. Image Commun. 47, 549–555 (2016)
Bashivan, P., Rish, I., Yeasin, M., Codella, N.: Learning representations from EEG with deep recurrent-convolutional neural networks (2015). arXiv preprint: arXiv:1511.06448
LeCun, Y., Bengio, Y.: Convolutional networks for images, speech, and time series. In: The Handbook of Brain Theory and Neural Networks, p. 3361 (1995)
Schirrmeister, R.T., et al.: Deep learning with convolutional neural networks for EEG decoding and visualization. Hum. Brain Mapp. 38, 5391–5420 (2017)
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)
Zeiler, M.D., Fergus, R.: Visualizing and understanding convolutional networks. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014, Part I. LNCS, vol. 8689, pp. 818–833. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10590-1_53
Gedeon, T., Catalan, J., Jin, J.: Image compression using shared weights and bidirectional networks. In: Proceedings 2nd International ICSC Symposium on Soft Computing (SOCO 1997), pp. 374–381 (1997)
Masci, J., Meier, U., Cireşan, D., Schmidhuber, J.: Stacked convolutional auto-encoders for hierarchical feature extraction. In: Honkela, T., Duch, W., Girolami, M., Kaski, S. (eds.) ICANN 2011, Part I. LNCS, vol. 6791, pp. 52–59. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-21735-7_7
Stober, S., Sternin, A., Owen, A.M., Grahn, J.A.: Deep feature learning for EEG recordings (2015). arXiv preprint: arXiv:1511.04306
Tabar, Y.R., Halici, U.: A novel deep learning approach for classification of EEG motor imagery signals. J. Neural Eng. 14, 016003 (2016)
Sykacek, P., Roberts, S.J.: Adaptive classification by variational Kalman filtering. In: Advances in Neural Information Processing Systems, pp. 753–760 (2003)
Krizhevsky, A., Hinton, G.: Learning multiple layers of features from tiny images (2009)
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Yao, Y., Plested, J., Gedeon, T. (2018). Deep Feature Learning and Visualization for EEG Recording Using Autoencoders. 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_50
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DOI: https://doi.org/10.1007/978-3-030-04239-4_50
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