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ELM Algorithm Optimized by WOA for Motor Imagery Classification

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Proceedings of ELM2019 (ELM 2019)

Part of the book series: Proceedings in Adaptation, Learning and Optimization ((PALO,volume 14))

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Abstract

The analysis of electroencephalogram (EEG) data is of considerable help to people with brain disease, and effective feature extraction classification approaches are needed to improve the recognition accuracy of EEG signals. In this paper, we propose an approach for EEG signal classification based on combination features and WOA-ELM algorithm. First, combination features take in account both principal component features by Principal Component Analysis (PCA) and label information of the training data by Linear Discriminant Analysis (LDA). Second, WOA-ELM algorithm is the optimized ELM algorithm to improve the ill-conditioned Single-hidden-Layer Feedforward neural Networks (SLFN) problem, the weights and biases between the input layer and the hidden layer of basic Extreme Learning Machine (ELM) are optimized by the Whale Optimization Algorithm (WOA) through bubble-net attacking strategy and shrinking encircling mechanism of humpback whales. The experimental results show that the highest classification accuracy of proposed method is 95.89% on motor imagery of BCI dataset. Compared with other methods, the proposed method has the competitive classification result.

This research is partially sponsored by National Natural Science Foundation of China (No. 61672070, 61572004), Beijing Municipal Natural Science Foundation (No. 4162058, 4202025), the Project of Beijing Municipal Education Commission (No. KM201910005008, KM201911232003), and Beijing Innovation Center for Future Chips (No. KYJJ2018004).

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References

  1. Alizadeh, B.B., Tabatabaei, Y.F., Shahidi, F., et al.: Principle component analysis (PCA) for investigation of relationship between population dynamics of microbial pathogenesis, chemical and sensory characteristics in beef slices containing Tarragon essential oil [J]. Microbial Pathogenesis 105, 37–50 (2017)

    Article  Google Scholar 

  2. Wang, S., Lu, J., Gu, X., et al.: Semi-supervised linear discriminant analysis for dimension reduction and classification. Pattern Recognition, 57(C), 179–189 (2016)

    Google Scholar 

  3. Richhariya, B., Tanveer, M.: EEG signal classification using universum support vector machine. Expert Systems with Applications (2018)

    Google Scholar 

  4. Huang, G.B., Zhu, Q., Siew, C.: Extreme learning machine: a new learning scheme of feedforward neural networks. In: Proceedings of the 2004 International Joint Conference on Neural Networks, vol. 2, pp. 985-990 (2004)

    Google Scholar 

  5. Zhu, W.T., Miao, J., Qing, L.Y.: Constrained extreme learning machine: a novel highly discriminative random feedforward neural network. In: 2014 International Joint Conference on Neural Networks (IJCNN2014). Beijing, 6–11 July 2014. United Stated, IEEE (2014)

    Google Scholar 

  6. Kasun, L.L.C., Zhou, H., Huang, G.B., et al.: Representational learning with ELMs for big data. Intell. Syst. IEEE 28(6), 31–34 (2013)

    Google Scholar 

  7. Tang, J., Deng, C., Huang, G.B.: Extreme learning machine for multilayer perceptron. IEEE Trans. Neural Netw. Learn. Syst. 27(4), 809–821 (2016)

    Article  MathSciNet  Google Scholar 

  8. Bao, M.: Classification of motor imagery and epileptic based on hierarchical extreme learning machine. Beijing University of Technology (2017)

    Google Scholar 

  9. Yanhui, X.: The application of extreme learning machine in feature extraction and classification of EEG Signals. Beijing University of Technology (2015)

    Google Scholar 

  10. Duan, L.J., Zhong, H.Y., Miao, J., et al.: A voting optimized strategy based on ELM for improving classification of motor imagery BCI data. Cogn. Comput. 6(3), 477–483 (2014)

    Article  Google Scholar 

  11. Duan, L., Bao, M., Miao, J., et al.: Classification based on multilayer extreme learning machine for motor imagery task from EEG signals. Procedia Comput. Sci. 88, 176–184 (2016)

    Article  Google Scholar 

  12. Duan, L., Bao, M., Cui, S., et al.: Motor imagery EEG classification based on kernel hierarchical extreme learning machine. Cogn. Computat. 9(6), 1–8 (2017)

    Google Scholar 

  13. Zeng, N., Zhang, H., Liu, W., et al.: A switching delayed PSO optimized extreme learning machine for short-term load forecasting. Neurocomputing 240, 175–182 (2017)

    Article  Google Scholar 

  14. Ling, Q.H., Song, Y.Q., Han, F., et al.: An improved learning algorithm for random neural networks based on particle swarm optimization and input-to-output sensitivity. Cogn. Syst. Res. S1389041717302929 (2018)

    Google Scholar 

  15. Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proceedings of the 1995 IEEE International Conference on Neural Networks, pp. 1942–1948 (1995)

    Google Scholar 

  16. Yang, X.-S.: A new metaheuristic bat-inspired algorithm. In: Proceedings of the Workshop on Nature Inspired Cooperative Strategies for Optimization (NICSO 2010). Springer, pp. 65–74 (2010)

    Google Scholar 

  17. Mirjalili, S., Lewis, A.: The whale optimization algorithm. Adv. Eng. Softw. 95, 51–67 (2016)

    Article  Google Scholar 

  18. Abdel-Basset, M., Gunasekaran, M., El-Shahat, D., et al.: A hybrid whale optimization algorithm based on local search strategy for the permutation flow shop scheduling problem. Future Generation Comput. Syst. 85, 103–105 (2018)

    Google Scholar 

  19. Mensh, B.D., Werfel, J., Seung, H.S.: BCI competition 2003-data set Ia: Combining gamma-band power with slow cortical potentials to improve single-trial classification of electroencephalographic signals. IEEE Trans. Biomed Eng. 51(6), 1052–1056 (2004)

    Article  Google Scholar 

  20. Sun, S., Zhang, C.: Assessing features for electroencephalographic signal categorization. IEEE International Conference on Acoustics, Speech, and Signal Processing, Proceedings. IEEE, 2005:v/417-v/420, vol. 5 (2005)

    Google Scholar 

  21. Wang, B., Jun, L., Bai, J., et al.: EEG recognition based on multiple types of information by using wavelet packet transform and neural networks. In: IEEE International Conference of the Engineering Medicine Biology Society 2005. IEEE-EMBS 2005, pp. 5377–5380 (2005)

    Google Scholar 

  22. Wu, T., Yan, G.Z., Yang, B.H., et al.: EEG feature extraction based on wavelet packet decomposition for brain computer interface. Measurement 41(6), 618–625 (2008)

    Article  Google Scholar 

  23. Kayikcioglu, T., Aydemir, O.: A polynomial fitting and k-NN based approach for improving classification of motor imagery BCI data. Elsevier Science Inc. (2010)

    Google Scholar 

  24. Duan, L.J., Zhang, Q., Yang, Z., Miao, J.: Research on heuristic feature extraction and classification of EEG signal based on BCI data set. Res. J. Appl. Sci. Eng. Technol. 5(3), 1008–1014 (2013)

    Article  Google Scholar 

  25. Qi, Z.: Classification of motor imagery-induced EEG signals. Master’s thesis, Beijing University of Technology, pp. 33–37 (2013)

    Google Scholar 

  26. Birbaumer, N.: Data sets IA for the BCI competition II. http://www.bbci.de/competition//ii/#datasets

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Author information

Authors and Affiliations

  1. Faculty of Information Technology, Beijing University of Technology, Beijing, China

    Lijuan Duan, Zhaoyang Lian, Juncheng Chen & Mingai Li

  2. Beijing Key Laboratory of Trusted Computing, Beijing, China

    Lijuan Duan & Zhaoyang Lian

  3. National Engineering Laboratory for Key Technologies of Information Security Level Protection, Beijing, China

    Lijuan Duan & Zhaoyang Lian

  4. Applied Sciences, Beijing University of Technology, Beijing, China

    Yuanhua Qiao

  5. Beijing Key Laboratory of Internet Culture and Digital Dissemination Research, School of Computer Science, Beijing Information Science and Technology University, Beijing, China

    Jun Miao

Authors
  1. Lijuan Duan
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  2. Zhaoyang Lian
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  3. Yuanhua Qiao
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  4. Juncheng Chen
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  5. Jun Miao
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  6. Mingai Li
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Corresponding author

Correspondence to Yuanhua Qiao .

Editor information

Editors and Affiliations

  1. Institute of Information and Control, Hangzhou Dianzi University, Zhejiang, China

    Jiuwen Cao

  2. Department of Computer and Information Science, University of Macau, Taipa, Macao

    Chi Man Vong

  3. Nokia Bell Labs, Espoo, Finland

    Yoan Miche

  4. Department of Information and Logistics Technology, University of Houston, Houston, TX, USA

    Amaury Lendasse

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Duan, L., Lian, Z., Qiao, Y., Chen, J., Miao, J., Li, M. (2021). ELM Algorithm Optimized by WOA for Motor Imagery Classification. In: Cao, J., Vong, C.M., Miche, Y., Lendasse, A. (eds) Proceedings of ELM2019. ELM 2019. Proceedings in Adaptation, Learning and Optimization, vol 14. Springer, Cham. https://doi.org/10.1007/978-3-030-58989-9_6

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