Effective user training for motor imagery based brain computer interface with object-directed 3D visual display

Shuang Liang, Kup Sze Choi, Jing Qin, Wai Man Pang, Pheng Ann Heng

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

3 Citations (Scopus)

Abstract

Effective user training could help us to improve the discrimination performance of our intention in brain computer interface (BCI). This paper aims to differentiate users left or right hand motor imagery (MI) tasks with different scenarios in 3D virtual environment, as non-object-directed (NOD) scenario, static-object-directed (SOD) scenario and dynamic-object-directed (DOD) scenario respectively. The results have significant differences by applying these three scenarios. Both SOD and DOD scenarios provide better classification accuracy, shorten single-trial period, and need smaller training samples comparing with the NOD case. We conclude that improving visual display may facilitate learning to use a BCI. Further comparing these results between single-subject and multiple-subject paradigm of BCI, we verify better classification performance could also be achieved by the multiple-subject paradigm. We believe these findings have the potential to improve discrimination performance of users intention for EEG-based BCI applications.
Original languageEnglish
Title of host publicationProceedings - 2014 7th International Conference on BioMedical Engineering and Informatics, BMEI 2014
PublisherIEEE
Pages297-301
Number of pages5
ISBN (Electronic)9781479958382
DOIs
Publication statusPublished - 1 Jan 2014
Event2014 7th International Conference on BioMedical Engineering and Informatics, BMEI 2014 - Bayshore Hotel, Dalian, China
Duration: 14 Oct 201416 Oct 2014

Conference

Conference2014 7th International Conference on BioMedical Engineering and Informatics, BMEI 2014
Country/TerritoryChina
CityDalian
Period14/10/1416/10/14

Keywords

  • Brain computer interface (BCI)
  • Electroencephalogram (EEG)
  • Motor imagery
  • Multiple-subject paradigm
  • Single-subject paradigm
  • User training
  • Visual display

ASJC Scopus subject areas

  • Signal Processing
  • Health Information Management
  • Information Systems
  • Biomedical Engineering
  • Health Informatics

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