Exploration of Data Dimensionality Reduction Methods for Improving Classification Performance of Voluntary Movements

  • Yanjuan Geng
  • Xing Kuang
  • Mingxing Zhu
  • Yi Zhang
  • Guanglin Li
  • Yuan-Ting Zhang
Conference paper
Part of the IFMBE Proceedings book series (IFMBE, volume 42)

Abstract

Electromyography (EMG)-based pattern recognition is one of the motor function restoration approaches for the amputees and the hemiparetic patients who suffered from stroke, spinal cord injury or brain injury. To improve the classification performance of multiple voluntary upper limb movements, high-density EMG was often used which may include some redundant information and increase the computational loads. For this reason, a common spatial filter (CSP)- based data dimensionality reduction method was proposed in this study, and the motion classification performance using multi-class CSP was compared with that when using universal principal component analysis (PCA) and the individual PCA. 22 classes of 56-channel EMG data that recorded from the upper limb of eight brain injured patients were used. The results showed that CSP decreased the motion classification error by 2.9% in comparison to that when using all EMG data, and the CSP was significantly better than the two PCA-based data dimensionality reduction methods in terms of classification error.

Keywords

CSP PCA pattern recognition high-density EMG brain injury 

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Yanjuan Geng
    • 1
  • Xing Kuang
    • 1
  • Mingxing Zhu
    • 1
  • Yi Zhang
    • 1
  • Guanglin Li
    • 1
  • Yuan-Ting Zhang
    • 1
    • 2
  1. 1.Shenzhen Institutes of Advanced Technology Key Lab of Health Informatics of Chinese Academy of SciencesChinese Academy of SciencesShenzhenChina
  2. 2.Department of Biomedical EngineeringChinese University of Hong KongHong KongChina

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