Abstract
Motor imagery (MI) is a mental process of a motor action including preparation for movement, passive observations of action and mental operations of motor representations. Brain computer interfaces can discriminate different status of individuals according to their EEG signals during imagery tasks. Power spectral density and common spatial patterns are both feature extraction methods that are commonly used to in the classification tasks of EEG series. In this paper, we combine recurrent neural networks and convolutional neural networks inspired by speech recognition and natural language processing. Furthermore, we apply deep models consist of stacking random forests to enhance the ability of feature representation and classification abilities for motor imagery EEG signals. Compared with traditional feature extraction methods, our approaches achieve significant improvements both in the MI-EEG dataset of BCI competitions with healthy individuals and the dataset collected from stroke patients.
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References
Classifications of Motor Imagery Tasks in Brain Computer Interface Using Linear Discriminant Analysis
Wang, Y., Gao, S., Gao, X.: Common spatial pattern method for channel selection in motor imagery based brain-computer interface. In: 27th Annual International Conference of the Engineering in Medicine and Biology Society, IEEE-EMBS 2005, pp. 5392–5395. IEEE (2006)
Ang, K.K., Chin, Z.Y., Zhang, H., et al.: Filter bank common spatial pattern (FBCSP) in brain-computer interface. In: IEEE International Joint Conference on Neural Networks, IJCNN 2008 (IEEE World Congress on Computational Intelligence), pp. 2390–2397. IEEE (2008)
Zheng, W.L., Zhu, J.Y., Peng, Y., et al.: EEG-based emotion classification using deep belief networks. In: IEEE International Conference on Multimedia and Expo (ICME), pp. 1–6. IEEE (2014)
Bashivan, P., Rish, I., Yeasin, M., et al.: Learning representations from EEG with deep recurrent-convolutional neural networks. arXiv preprint arXiv:1511.06448 (2015)
Leamy, D.J., Kocijan, J., Domijan, K., et al.: An exploration of EEG features during recovery following stroke–implications for BCI-mediated neurorehabilitation therapy. J. Neuroeng. Rehabil. 11(1), 9 (2014)
Cichocki, A., Mandic, D., De Lathauwer, L., et al.: Tensor decompositions for signal processing applications: from two-way to multiway component analysis. IEEE Sig. Process. Mag. 32(2), 145–163 (2015)
Zhao, Q., Caiafa, C.F., Cichocki, A., Zhang, L., Phan, A.H.: Slice oriented tensor decomposition of EEG data for feature extraction in space, frequency and time domains. In: Leung, C.S., Lee, M., Chan, J.H. (eds.) ICONIP 2009. LNCS, vol. 5863, pp. 221–228. Springer, Heidelberg (2009). doi:10.1007/978-3-642-10677-4_25
Zhou, Z.H., Feng, J.: Deep forest: towards an alternative to deep neural networks. arXiv preprint arXiv:1702.08835 (2017)
Waibel, A., Hanazawa, T., Hinton, G., et al.: Phoneme recognition using time-delay neural networks. IEEE Trans. Acoust. Speech Sig. Process. 37(3), 328–339 (1989)
Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)
Millán, J. del R.: On the need for on-line learning in brain-computer interfaces. In: Proceedings of Intrelational Joint Conference on Neural Networks (2004)
Lai, S., Xu, L., Liu, K., et al.: Recurrent convolutional neural networks for text classification. In: AAAI, vol. 333, pp. 2267–2273 (2015)
Amodei, D., Anubhai, R., Battenberg, E., et al.: Deep speech 2: end-to-end speech recognition in English and mandarin. In: International Conference on Machine Learning, pp. 173–182 (2016)
Acknowledgement
This paper is supported by the 863 National High Technology Research and Development Program of China (SS2015AA020501), the Basic Research Project of “Innovation Action Plan” (16JC1402800), the Major Basic Research Program (15JC1400103) of Shanghai Science and Technology Committee and the interdisciplinary Program of Shanghai Jiao Tong University (YG2015MS43).
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Shen, Y., Lu, H., Jia, J. (2017). Classification of Motor Imagery EEG Signals with Deep Learning Models. In: Sun, Y., Lu, H., Zhang, L., Yang, J., Huang, H. (eds) Intelligence Science and Big Data Engineering. IScIDE 2017. Lecture Notes in Computer Science(), vol 10559. Springer, Cham. https://doi.org/10.1007/978-3-319-67777-4_16
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DOI: https://doi.org/10.1007/978-3-319-67777-4_16
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