Abstract
In this paper, a new EMD adaptive decomposition algorithm is designed to denoise the original EEGÂ signals, and a deep neural network model ConvLSTM is used to extract the features of the denoised signals. First, EEG signals are collected by a brain equipment. Then we use the proposed method to denoise the collected signals. Finally, the needed features are extracted with the convLSTM. Compared with previous methods, this proposed algorithm can extract the temporal and spatial characteristics of EEG more effectively. The proposed method is implemented on the actual moving EEG dataset, which verifies the validity and practicability of the proposed model.
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
Hamaneh, M.B., Chitravas, N., Kaiboriboon, K., et al.: Automated removal of EKG artifact from EEG data using independent component analysis and continuous wavelet transformation. J. IEEE Trans. Biomed. Eng. 61, 1634–1641 (2014)
Mammone, N., La Foresta, F., Morabito, F.C.: Automatic artifact rejection from multichannel scalp EEG by wavelet ICA. J. IEEE Sens. J. 12, 533–542 (2012)
Akhtar, M.T., Mitsuhashi, W., James, C.J.: Employing spatially constrained ICA and wavelet denoising, for automatic removal of artifacts from multichannel EEG data. J. Sig. Process. 92, 401–416 (2012)
Geetha, G., Geethalakshmi, S.N.: Artifact removal from EEG using spatially constrained fastica and fuzzy shrink thresholding technique. J. Procedia Eng. 30, 1064–1071 (2012)
Babu, P.A., Prasad, K.: Removal of ocular artifacts from EEG signals using adaptive threshold PCA and wavelet transforms. In: 2011 IEEE International Conference on Communication Systems and Network Technologies, pp. 572–575. IEEE Press, Katra (2011)
Zhang, L., Bao, P., Wu, X.: Multiscale LMMSE-based image denoising with optimal wavelet selection. J. IEEE Trans. Circ. Syst. Video Technol. 15, 469–481 (2005)
Huang, N.E., Shen, Z., Long, S.R., et al.: The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. J. Proceed. R. Soc. Lond. 454, 903–995 (1998)
Hinton, G.E., Osindero, S., The, Y.W.: A fast learning algorithm for deep belief nets. J. Neural Comput. 18, 1527–1554 (2006)
Millán, J.D.R.: Adaptive brain interfaces. J. Commun. ACM 46, 219–227 (1999)
Hazarika, N., Chen, J.Z., Tsoi, A.C., et al.: Classification of EEG signals using the wavelet transform. J. Sig. Process. 59, 61–72 (1997)
Cai, M., Hu, P.: Task classification of right-hand and foot motion imagery based on wavelet packet transform. Chin. J. Med. Ins. 41, 177–180 (2017)
Liu, C., Zhao, H.B., Chun-Sheng, L.I., et al.: CSP/SVM-based EEG classification of imagined hand movements. J. Northeast. Univ. 31, 1098–1101 (2010)
Mao, Z., Yao, W.X., Huang, Y.: EEG-based biometric identification with deep learning. In: 8th International IEEE/EMBS Conference on Neural Engineering, pp. 609–612. IEEE Press, Shanghai (2011)
Ahmedt-Aristizabal, D., Fookes, C., Nguyen, K., et al.: Deep classification of epileptic signals. In: 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 332–335. IEEE Press, Hawaii (2018)
Xingjian, S.H.I., Chen, Z., Wang, H., et al.: Convolutional LSTM network: A machine learning approach for precipitation nowcasting. In: 29th Conference on Neural Information Processing Systems, pp. 802–810. Montreal (2015)
Acknowledgments
The work was supported by National Natural Science Foundation of China (No. 61433003, No.61621063).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Lv, H., Ren, X., Lv, Y. (2020). EEG Recognition with Adaptive Noise Reduction Based on Convolutional LSTM Network. In: Wang, R., Chen, Z., Zhang, W., Zhu, Q. (eds) Proceedings of the 11th International Conference on Modelling, Identification and Control (ICMIC2019). Lecture Notes in Electrical Engineering, vol 582. Springer, Singapore. https://doi.org/10.1007/978-981-15-0474-7_22
Download citation
DOI: https://doi.org/10.1007/978-981-15-0474-7_22
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-15-0473-0
Online ISBN: 978-981-15-0474-7
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)