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.
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Acknowledgments
The work was supported by National Natural Science Foundation of China (No. 61433003, No.61621063).
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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
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DOI: https://doi.org/10.1007/978-981-15-0474-7_22
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