EEG Recognition with Adaptive Noise Reduction Based on Convolutional LSTM Network

Hengxing Lv; Xuemei Ren; Yongfeng Lv

doi:10.1007/978-981-15-0474-7_22

EEG Recognition with Adaptive Noise Reduction Based on Convolutional LSTM Network

Hengxing Lv, Xuemei Ren^*, Yongfeng Lv

^*此作品的通讯作者

自动化学院

Beijing Institute of Technology

科研成果: 书/报告/会议事项章节 › 会议稿件 › 同行评审

2 引用（Scopus）

摘要

In this paper, a new EMD adaptive decomposition algorithm is designed to denoise the original EEG 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.

源语言	英语
主期刊名	Proceedings of the 11th International Conference on Modelling, Identification and Control, ICMIC 2019
编辑	Rui Wang, Zengqiang Chen, Weicun Zhang, Quanmin Zhu
出版商	Springer
页	227-237
页数	11
ISBN（印刷版）	9789811504730
DOI	https://doi.org/10.1007/978-981-15-0474-7_22
出版状态	已出版 - 2020
活动	11th International Conference on Modelling, Identification and Control, ICMIC 2019 - Tianjin, 中国期限: 13 7月 2019 → 15 7月 2019

出版系列

姓名	Lecture Notes in Electrical Engineering
卷	582
ISSN（印刷版）	1876-1100
ISSN（电子版）	1876-1119

会议

会议	11th International Conference on Modelling, Identification and Control, ICMIC 2019
国家/地区	中国
市	Tianjin
时期	13/07/19 → 15/07/19

访问文件

10.1007/978-981-15-0474-7_22

其它文件与链接

链接到 Scopus 的出版物

引用此

Lv, H., Ren, X., & Lv, Y. (2020). EEG Recognition with Adaptive Noise Reduction Based on Convolutional LSTM Network. 在 R. Wang, Z. Chen, W. Zhang, & Q. Zhu (编辑), Proceedings of the 11th International Conference on Modelling, Identification and Control, ICMIC 2019 (页码 227-237). (Lecture Notes in Electrical Engineering; 卷 582). Springer. https://doi.org/10.1007/978-981-15-0474-7_22

Lv, Hengxing ; Ren, Xuemei ; Lv, Yongfeng. / EEG Recognition with Adaptive Noise Reduction Based on Convolutional LSTM Network. Proceedings of the 11th International Conference on Modelling, Identification and Control, ICMIC 2019. 编辑 / Rui Wang ; Zengqiang Chen ; Weicun Zhang ; Quanmin Zhu. Springer, 2020. 页码 227-237 (Lecture Notes in Electrical Engineering).

@inproceedings{25a3af15297548c19f4820bc28126301,

title = "EEG Recognition with Adaptive Noise Reduction Based on Convolutional LSTM Network",

abstract = "In this paper, a new EMD adaptive decomposition algorithm is designed to denoise the original EEG 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.",

keywords = "ConvLSTM, Deep learning, Electroencephalogram, Empirical mode decomposition",

author = "Hengxing Lv and Xuemei Ren and Yongfeng Lv",

note = "Publisher Copyright: {\textcopyright} 2020, Springer Nature Singapore Pte Ltd.; 11th International Conference on Modelling, Identification and Control, ICMIC 2019 ; Conference date: 13-07-2019 Through 15-07-2019",

year = "2020",

doi = "10.1007/978-981-15-0474-7_22",

language = "English",

isbn = "9789811504730",

series = "Lecture Notes in Electrical Engineering",

publisher = "Springer",

pages = "227--237",

editor = "Rui Wang and Zengqiang Chen and Weicun Zhang and Quanmin Zhu",

booktitle = "Proceedings of the 11th International Conference on Modelling, Identification and Control, ICMIC 2019",

address = "Germany",

}

Lv, H, Ren, X & Lv, Y 2020, EEG Recognition with Adaptive Noise Reduction Based on Convolutional LSTM Network. 在 R Wang, Z Chen, W Zhang & Q Zhu (编辑), Proceedings of the 11th International Conference on Modelling, Identification and Control, ICMIC 2019. Lecture Notes in Electrical Engineering, 卷 582, Springer, 页码 227-237, 11th International Conference on Modelling, Identification and Control, ICMIC 2019, Tianjin, 中国, 13/07/19. https://doi.org/10.1007/978-981-15-0474-7_22

EEG Recognition with Adaptive Noise Reduction Based on Convolutional LSTM Network. / Lv, Hengxing; Ren, Xuemei; Lv, Yongfeng.
Proceedings of the 11th International Conference on Modelling, Identification and Control, ICMIC 2019. 编辑 / Rui Wang; Zengqiang Chen; Weicun Zhang; Quanmin Zhu. Springer, 2020. 页码 227-237 (Lecture Notes in Electrical Engineering; 卷 582).

科研成果: 书/报告/会议事项章节 › 会议稿件 › 同行评审

TY - GEN

T1 - EEG Recognition with Adaptive Noise Reduction Based on Convolutional LSTM Network

AU - Lv, Hengxing

AU - Ren, Xuemei

AU - Lv, Yongfeng

PY - 2020

Y1 - 2020

N2 - In this paper, a new EMD adaptive decomposition algorithm is designed to denoise the original EEG 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.

AB - In this paper, a new EMD adaptive decomposition algorithm is designed to denoise the original EEG 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.

KW - ConvLSTM

KW - Deep learning

KW - Electroencephalogram

KW - Empirical mode decomposition

UR - http://www.scopus.com/inward/record.url?scp=85076864072&partnerID=8YFLogxK

U2 - 10.1007/978-981-15-0474-7_22

DO - 10.1007/978-981-15-0474-7_22

M3 - Conference contribution

AN - SCOPUS:85076864072

SN - 9789811504730

T3 - Lecture Notes in Electrical Engineering

SP - 227

EP - 237

BT - Proceedings of the 11th International Conference on Modelling, Identification and Control, ICMIC 2019

A2 - Wang, Rui

A2 - Chen, Zengqiang

A2 - Zhang, Weicun

A2 - Zhu, Quanmin

PB - Springer

T2 - 11th International Conference on Modelling, Identification and Control, ICMIC 2019

Y2 - 13 July 2019 through 15 July 2019

ER -

EEG Recognition with Adaptive Noise Reduction Based on Convolutional LSTM Network

摘要

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