An Electroencephalogram Signal Processing Method Fusing Wavelet Packet and Neural Network

doi:10.13190/j.jbupt.2020-208

Journal of Beijing University of Posts and Telecommunications ›› 2021, Vol. 44 ›› Issue (3): 94-99.doi: 10.13190/j.jbupt.2020-208

• REPORTS • Previous Articles Next Articles

An Electroencephalogram Signal Processing Method Fusing Wavelet Packet and Neural Network

LI Duan-ling¹, CHENG Li-wei¹, YU Gong-jing², ZHANG Zhong-hai², YU Shu-yue²

1. School of Modern Post(School of Automation), Beijing University of Posts and Telecommunications, Beijing 100876, China;
2. Beijing Aerospace Measurement & Control Technology Company Limited, Beijing 100041, China

Received:2020-10-12 Online:2021-06-28 Published:2021-06-23

Abstract

Abstract: Aiming at the classification accuracy of the motor imagery electroencephalogram in processing is low, a processing method based on the combination of energy (second-order moment) wavelet packet transform and Levenberg-Marquardt neural network is proposed. Firstly, the energy method is used to analyze signal in the time domain, and the effective time sequence is selected. Then, wavelet packet transform is used to decompose the time-frequency of each pilot signal in the selected effective time-domain segment, and the frequency information related to the imagination task is selected to reconstruct the signal characteristics. Finally, the features reconstructed by each guide signal are concatenated and imported into the neural network based on the Levenberg-Marquardt training algorithm to realize the task classification. The method was verified by two kinds of electroencephalogram signal standard competition database, and the classification accuracy is 95.62% and 90.13%, respectively. Compared with some recent research results, this algorithm has a better processing effect.

Key words: motor imagery electroencephalogram, second-order moment, wavelet packet transformation, back propagation neural network, Levenberg-Marquardt algorithm

CLC Number:

TP751.2

LI Duan-ling, CHENG Li-wei, YU Gong-jing, ZHANG Zhong-hai, YU Shu-yue. An Electroencephalogram Signal Processing Method Fusing Wavelet Packet and Neural Network[J]. Journal of Beijing University of Posts and Telecommunications, 2021, 44(3): 94-99.

References

[1] Vaughan T M, Heetderks W J, Trejo L J, et al. Brain-computer interface technology:a review of the second international meeting[J]. IEEE Transactions on Neural Systems & Rehabilitation Engineering:a Publication of the IEEE Engineering in Medicine & Biology Society, 2003, 11(2):94-109.
[2] Nguyen L B, Nguyen A V, Ling S H, et al. Combining genetic algorithm and Levenberg-Marquardt algorithm in training neural network for hypoglycemia detection using EEG signals[C]//2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). New York:IEEE Press, 2013:5386-5389.
[3] Aydemir, Onder. Combining sub-band power features extracted from different time segments of EEG trials[C]//2017 40th International Conference on Telecommunications and Signal Processing (TSP). New York:IEEE Press, 2017:383-386.
[4] Chatterjee R, Bandyopadhyay T, Sanyal D K, et al. Dimensionality reduction of EEG signal using fuzzy discerni-bility matrix[C]//2017 10th International Conference on Human System Interactions (HSI). New York:IEEE Press, 2017:131-136.
[5] Nguyen P, Tran D, Le T, et al. Multi-sphere support vector data description for brain-computer interface[C]//2012 Fourth International Conference on Communications and Electronics (ICCE). New York:IEEE Press, 2012:318-321.
[6] Saka K, Aydemir Ö, Öztürk M. Classification of EEG signals recorded during right/left hand movement imagery using fast Walsh Hadamard transform based features[C]//2016 39th International Conference on Telecommunications and Signal Processing (TSP). New York:IEEE Press, 2016:413-416.
[7] Guo Xiaojing, Wu Xiaopei. Motor imagery EEG classification based on dynamic ICA mixing matrix[C]//2010 4th International Conference on Bioinformatics and Biomedical Engineering. New York:IEEE Press, 2010:1-4.
[8] Schlögl A, Lugger K, Pfurtscheller G. Using adaptive autoregressive parameters for a brain-computer-interface experiment[C]//19th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. ‘Magnificent Milestones and Emerging Opportunities in Medical Engineering’(Cat.No.97CH36136). New York:IEEE Press, 1997:1533-1535.
[9] Cheng Liwei, Li Duanling, Li Xiang, et al. The optimal wavelet basis function selection in feature extraction of motor imagery electroencephalogram based on wavelet packet transformation[J]. IEEE Access, 2019, 7:174465-174481.
[10] Blankertz B. BCI competition Ⅱ final results[EB/OL]. (2003-05-02)[2020-10-02]. http://bbci.de/competition/ii/results/index.html.
[11] Blankertz B. BCI Competition Ⅳ final results[EB/OL]. (2008-12-12)[2020-10-02]. http://bbci.de/competition/iv/results/index.html.
[12] Bentlemsan M, Zemouri E T T, Bouchaffra D, et al. Random forest and filter bank common spatial patterns for EEG-based motor imagery classification[C]//2014 5th International Conference on Intelligent Systems, Modelling and Simulation. New York:IEEE Press, 2015:235-238.
[13] Bustios P, Rosa J L. Restricted exhaustive search for frequency band selection in motor imagery classification[C]//2017 International Joint Conference on Neural Networks (IJCNN). New York:IEEE Press, 2017:4208-4213.
[14] Muthong S, Vateekul P, Sriyudthsak M. Stacked probabilistic regularized LDA on partitioning non-stationary EEG data for left/right hand imagery classification[C]//2016 IEEE EMBS Conference on Biomedical Engineering and Sciences (IECBES). New York:IEEE Press, 2016:301-306.
[15] Tian Geliang, Liu Yue. Study on classification of left-right hands motor imagery EEG signals based on CNN[C]//2018 IEEE 17th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC). New York:IEEE Press, 2018:324-329.
[16] Zhang Yu, Nam Chang S, Zhou Guoxu, et al. Temporally constrained sparse group spatial patterns for motor imagery BCI[J]. IEEE Transactions on Cybernetics, 2018, 49(9):3322-3332.

An Electroencephalogram Signal Processing Method Fusing Wavelet Packet and Neural Network

RichHTML

Knowledge

Abstract

Cite this article

share this article

References

Related Articles 12

Recommended Articles

Metrics

Comments

[1]	YANG Lihua, NIE Qian, HU Bo, JIANG Ting. Channel Prediction Method Joint BP Neural Network with Basis Expansion Model [J]. Journal of Beijing University of Posts and Telecommunications, 2023, 46(3): 13-18.
[2]	YI Qian, LI Wei, YI Shu-ping, XIE Jun-dong. Trustworthy Identity Authentication Based on Joint Time-Frequency Analysis of Mouse Behavior [J]. Journal of Beijing University of Posts and Telecommunications, 2021, 44(4): 121-128.
[3]	QI Tian-tian, CHEN Yao, HE Cai-hou, LONG Sheng-rong, LI Qiu-feng. A Wavelet Packet Neural Network Feature Recognition Method for Damage Acoustic Emission Signals [J]. Journal of Beijing University of Posts and Telecommunications, 2021, 44(1): 124-130.
[4]	DAI Jian-yong, DENG Xian-hong, WANG Bin, WANG Heng-hao. Clustering Routing Protocol for WSNs Based on Neural Network Optimization by Improved Firefly Algorithm [J]. Journal of Beijing University of Posts and Telecommunications, 2020, 43(3): 131-137.
[5]	JIANG Fang, ZHANG Nan-fei, HU Yan-jun, WANG Yi. BP Neural Network Based CSI Device-Free Target Classification Method [J]. Journal of Beijing University of Posts and Telecommunications, 2020, 43(1): 40-45.
[6]	ZHAO Xiong-wen, SUN Ning-yao, GENG Sui-yan, ZHANG Yu, DU Fei. Time-Varying Channel Modeling Using Least Square Support Vector Machine [J]. JOURNAL OF BEIJING UNIVERSITY OF POSTS AND TELECOM, 2019, 42(5): 29-35.
[7]	YAO Yun-zheng, YANG An, SHI Zhi-qiang, SUN Li-min. Application of BP Neural Network Model in Device State Detection of Industrial Control System [J]. JOURNAL OF BEIJING UNIVERSITY OF POSTS AND TELECOM, 2016, 39(s1): 14-18.
[8]	HU Yan-zhu, LI Lei-yuan. The Application of Kalman Filtering-BP Neural Network in Autonomous Positioning of End-Effector [J]. JOURNAL OF BEIJING UNIVERSITY OF POSTS AND TELECOM, 2016, 39(6): 110-115.
[9]	QI Feng, LI Qi, HAN Qian, DU Yi, QIU Xue-song. A Neural Network Based Method to Assess Electric Power Communication Network Risk [J]. JOURNAL OF BEIJING UNIVERSITY OF POSTS AND TELECOM, 2014, 37(1): 90-93.
[10]	DIAO Ming, AN Chun-lian. A Novel DOA Estimation Method in Impulsive Noise [J]. JOURNAL OF BEIJING UNIVERSITY OF POSTS AND TELECOM, 2013, 36(5): 99-104.
[11]	ZHANG Lei;CHEN Jun-liang;MENG Xiang-wu;SHEN Xiao-yan;DUAN Kun. BP Neural Networks-Based Collaborative Filtering Recommendation Algorithm [J]. JOURNAL OF BEIJING UNIVERSITY OF POSTS AND TELECOM, 2009, 32(6): 42-46.
[12]	YU Yan-hua, SONG Mei, PAN Yang-fa, SONG Jun-de . An Improved Network Performance Evaluation Method Based on Support Vector Machines [J]. JOURNAL OF BEIJING UNIVERSITY OF POSTS AND TELECOM, 2007, 30(6): 85-88.