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作者:

Li, Ming-ai (Li, Ming-ai.) (学者:李明爱) | Luo, Xin-yong (Luo, Xin-yong.) | Yang, Jin-fu (Yang, Jin-fu.) (学者:杨金福)

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摘要:

When performing studies on brain computer interface based rehabilitation problems, researchers frequently encounter difficulty due to the curse of dimensionality and the nonlinear nature of Motor Imagery Electroencephalography (MI-EEG). Though many approaches have been proposed recently to address the feature extraction problem and have shown surprising performance, unfortunately, most of them are non-parametric or linear dimension reduction techniques, which are limited in utility for out of-sample extension for MI-EEG classification. To address the problem and obtain accurate MI-EEG features, a new unsupervised nonlinear dimensionality reduction technique termed parametric t-Distributed Stochastic Neighbor Embedding (P. t-SNE) is employed to extract the nonlinear features from MI-EEG. Considering that MI-EEG is a kind of non-stationary signal with remarkable time-frequency rhythmic distribution characteristics, Discrete Wavelet Transform (DWT) is used to extract the time frequency features of MI-EEG. Furthermore, P. t-SNE is applied to selected wavelet components to get the nonlinear features. They are then combined serially to construct the feature vector. Experiments are conducted on a publicly available dataset, and the experimental results show that the nonlinear features have great visualization performance with obvious clustering distribution, and the feature extraction method indicates excellent classification performance as evaluated by a support vector machine classifier. This paper suggests a manifold based technique for further analysis and classification research of MI EEG. (C) 2016 Elsevier B.V. All rights reserved.

关键词:

Visualization Motor imagery electroencephalography Feature extraction Parametric t-Distributed Stochastic Neighbor Embedding Discrete wavelet transform

作者机构:

  • [ 1 ] [Li, Ming-ai]Beijing Univ Technol, Coll Elect Informat & Control Engn, Beijing 100124, Peoples R China
  • [ 2 ] [Luo, Xin-yong]Beijing Univ Technol, Coll Elect Informat & Control Engn, Beijing 100124, Peoples R China
  • [ 3 ] [Yang, Jin-fu]Beijing Univ Technol, Coll Elect Informat & Control Engn, Beijing 100124, Peoples R China

通讯作者信息:

  • 李明爱

    [Li, Ming-ai]Beijing Univ Technol, Coll Elect Informat & Control Engn, Beijing 100124, Peoples R China

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来源 :

NEUROCOMPUTING

ISSN: 0925-2312

年份: 2016

卷: 218

页码: 371-381

6 . 0 0 0

JCR@2022

ESI学科: COMPUTER SCIENCE;

ESI高被引阀值:167

中科院分区:3

被引次数:

WoS核心集被引频次: 29

SCOPUS被引频次: 31

ESI高被引论文在榜: 0 展开所有

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