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Deep Extreme Learning Machine and Its Application in EEG Classification

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  • Shifei Ding
  • Nan Zhang
  • Xinzheng Xu
  • Lili Guo
  • Jian Zhang

Abstract

Recently, deep learning has aroused wide interest in machine learning fields. Deep learning is a multilayer perceptron artificial neural network algorithm. Deep learning has the advantage of approximating the complicated function and alleviating the optimization difficulty associated with deep models. Multilayer extreme learning machine (MLELM) is a learning algorithm of an artificial neural network which takes advantages of deep learning and extreme learning machine. Not only does MLELM approximate the complicated function but it also does not need to iterate during the training process. We combining with MLELM and extreme learning machine with kernel (KELM) put forward deep extreme learning machine (DELM) and apply it to EEG classification in this paper. This paper focuses on the application of DELM in the classification of the visual feedback experiment, using MATLAB and the second brain-computer interface (BCI) competition datasets. By simulating and analyzing the results of the experiments, effectiveness of the application of DELM in EEG classification is confirmed.

Suggested Citation

  • Shifei Ding & Nan Zhang & Xinzheng Xu & Lili Guo & Jian Zhang, 2015. "Deep Extreme Learning Machine and Its Application in EEG Classification," Mathematical Problems in Engineering, Hindawi, vol. 2015, pages 1-11, May.
    • Handle: RePEc:hin:jnlmpe:129021
    DOI: 10.1155/2015/129021
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    Cited by:

    1. Mumin Zhang & Yuzhi Wang & Haochen Zhang & Zhiyun Peng & Junjie Tang, 2023. "A Novel and Robust Wind Speed Prediction Method Based on Spatial Features of Wind Farm Cluster," Mathematics, MDPI, vol. 11(3), pages 1-17, January.
    2. Weiyu Wang & Xunxin Zhao & Lijun Luo & Pei Zhang & Fan Mo & Fei Chen & Diyi Chen & Fengjiao Wu & Bin Wang, 2022. "A Fault Diagnosis Method of Rolling Bearing Based on Attention Entropy and Adaptive Deep Kernel Extreme Learning Machine," Energies, MDPI, vol. 15(22), pages 1-19, November.
    3. Chunyang Xia & Zengxi Pan & Joseph Polden & Huijun Li & Yanling Xu & Shanben Chen, 2022. "Modelling and prediction of surface roughness in wire arc additive manufacturing using machine learning," Journal of Intelligent Manufacturing, Springer, vol. 33(5), pages 1467-1482, June.

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