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Mathematical Problems in Engineering
Volume 2015, Article ID 129021, 11 pages
Research Article

Deep Extreme Learning Machine and Its Application in EEG Classification

1School of Computer Science and Technology, China University of Mining and Technology, Xuzhou 221116, China
2Key Laboratory of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China

Received 26 August 2014; Revised 4 November 2014; Accepted 12 November 2014

Academic Editor: Amaury Lendasse

Copyright © 2015 Shifei Ding et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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.