title: The hybrid BCI system for movement control by combining motor imagery and moving onset visual evoked potential.
abstract: Movement control is an important application for EEG-BCI (EEG-based brain-computer interface) systems. A single-modality BCI cannot provide an efficient and natural control strategy, but a hybrid BCI system that combines two or more different tasks can effectively overcome the drawbacks encountered in single-modality BCI control.
  
  
  
  In the current paper, we developed a new hybrid BCI system by combining MI (motor imagery) and mVEP (motion-onset visual evoked potential), aiming to realize the more efficient 2D movement control of a cursor.
  
  
  
  The offline analysis demonstrates that the hybrid BCI system proposed in this paper could evoke the desired MI and mVEP signal features simultaneously, and both are very close to those evoked in the single-modality BCI task. Furthermore, the online 2D movement control experiment reveals that the proposed hybrid BCI system could provide more efficient and natural control commands.
  
  
  
  The proposed hybrid BCI system is compensative to realize efficient 2D movement control for a practical online system, especially for those situations in which P300 stimuli are not suitable to be applied.
authors.affiliation: Key Laboratory for NeuroInformation of Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, People's Republic of China.
authors.forename: Teng
authors.initials: T
authors.lastname: Ma
authors.forename: Hui
authors.initials: H
authors.lastname: Li
authors.forename: Lili
authors.initials: L
authors.lastname: Deng
authors.forename: Hao
authors.initials: H
authors.lastname: Yang
authors.forename: Xulin
authors.initials: X
authors.lastname: Lv
authors.forename: Peiyang
authors.initials: P
authors.lastname: Li
authors.forename: Fali
authors.initials: F
authors.lastname: Li
authors.forename: Rui
authors.initials: R
authors.lastname: Zhang
authors.forename: Tiejun
authors.initials: T
authors.lastname: Liu
authors.forename: Dezhong
authors.initials: D
authors.lastname: Yao
authors.forename: Peng
authors.initials: P
authors.lastname: Xu
mesh_terms: D000328:Adult; D062207:Brain-Computer Interfaces; D002540:Cerebral Cortex / Q000502:physiology*; D004569:Electroencephalography / Q000379:methods*; D005074:Evoked Potentials, Visual / Q000502:physiology*; D005260:Female; D006801:Humans; D007092:Imagination / Q000502:physiology*; D008297:Male; D009068:Movement / Q000502:physiology*; D011597:Psychomotor Performance / Q000502:physiology; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D018511:Systems Integration; D014796:Visual Perception / Q000502:physiology*