Performance Evaluation of Walking Imagery Training Based on Virtual Environment in Brain-Computer Interfaces

Xiaolu Liu, Shuang Liang, Wenlong Hang, Baiying Lei, Qiong Wang, Jing Qin, Kup Sze Choi

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

3 Citations (Scopus)


Motor imagery (MI) based on brain computer interfaces (BCIs) have been widely applied for upper limb motor rehabilitation. Due to the fact that a large number of disabled people need to restore or improve walking ability, it is also important to investigate the use of MI-based BCIs for lower limb motor rehabilitation. The brain activity of lower limb MI is more difficult to detect because of low reliability. The purpose of this study is to find a suitable paradigm of walking imagery to achieve better training effect and ensure reliable brain activity. We developed the text-based paradigm and the virtual environment (VE)-based paradigm, and evaluated their performance on identifying walking imagery from idle state.The experimental results provide evidences that the VE-based paradigm could improve the average classification accuracy. This paradigm would induce EEG patterns that make them easier for single-trial detection of walking imagery. This study has the potential to improve the reliability and robustness of walking imagery based BCIs.
Original languageEnglish
Title of host publicationProceedings - 2017 IEEE International Symposium on Multimedia, ISM 2017
Number of pages6
ISBN (Electronic)9781538629369
Publication statusPublished - 28 Dec 2017
Event19th IEEE International Symposium on Multimedia, ISM 2017 - Taichung, Taiwan
Duration: 11 Dec 201713 Dec 2017


Conference19th IEEE International Symposium on Multimedia, ISM 2017

ASJC Scopus subject areas

  • Media Technology
  • Sensory Systems

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