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International Journal of Fuzzy Systems

, Volume 19, Issue 2, pp 566–579 | Cite as

EEG Classification of Imaginary Lower Limb Stepping Movements Based on Fuzzy Support Vector Machine with Kernel-Induced Membership Function

  • Wei-Chun Hsu
  • Li-Fong Lin
  • Chun-Wei Chou
  • Yu-Tsung Hsiao
  • Yi-Hung LiuEmail author
Article

Abstract

Although various kinds of motor imageries have been used for BCI applications, imaginary lower limb stepping movement has not been studied yet. The purpose of this study is to investigate the possibilities of using electroencephalography (EEG) signal to classify imaginary lower limb stepping movements and to design a robust motor imagery classifier based on support vector machine (SVM). A cue-based experimental paradigm is designed to record nine-channel EEG associated with imaginary left leg stepping (L-stepping) and right leg stepping (R-stepping) movements from eight healthy subjects. Features including band powers (BPs), common spatial pattern (CSP), and a filter-bank CSP (FB-CSP) were extracted from the recorded EEG. Fuzzy SVM (FSVM) is introduced to this study to classify L-stepping and R-stepping imageries. We propose a novel kernel-induced membership function to address the issue of data relative importance assignment. The FSVM with the membership function suggested in the original work of FSVM (Type-I FSVM) and the FSVM with the one we proposed (Type-II FSVM) is compared. Results indicated that the classification accuracies based on BP features are near the chance level (~50 %). Both alpha-band CSP (71.25 %) and FB-CSP (75.63 %) gave acceptable results as a simple k-NN classifier is performed. Results show that both types of FSVM performed better than the conventional SVM. Also, Type-II FSVM outperforms Type-I FSVM, especially when the alpha-CSP feature is employed, where the improvement in error reduction rate is over 15 %. The highest average L-stepping versus R-stepping classification accuracy over the eight subjects is achieved (86.25 % in single-trial analysis) by FB-CSP and FSVM-II. The high classification result suggests the feasibility of using lower limb stepping imagery to develop a BCI that can control devices or might be able to serve as a neurofeedback tool for users who need lower limb stepping imagery training for gait function improvement.

Keywords

EEG Brain–computer interface Motor imagery Lower limb stepping Support vector machine Common spatial pattern 

Notes

Acknowledgments

This work was supported by the Ministry of Science and Technology (MOST), Taiwan, under Grant No. 103-2923-E-027-001-MY3.

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Copyright information

© Taiwan Fuzzy Systems Association and Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  1. 1.Institute of Biomedical EngineeringNational Taiwan University of Science and TechnologyTaipeiTaiwan
  2. 2.Department of Physical Medicine and Rehabilitation, Shuang Ho HospitalTaipei Medical UniversityTaipeiTaiwan
  3. 3.Institute of Gerontology and Health ManagementTaipei Medical UniversityTaipeiTaiwan
  4. 4.Department of Mechanical EngineeringChung Yuan Christian UniversityChungliTaiwan
  5. 5.Institute of Mechatronic EngineeringNational Taipei University of TechnologyTaipeiTaiwan
  6. 6.Department of Mechanical EngineeringNational Taipei University of TechnologyTaipeiTaiwan

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