• DocumentCode
    68273
  • Title

    Towards Noninvasive Hybrid Brain–Computer Interfaces: Framework, Practice, Clinical Application, and Beyond

  • Author

    Muller-Putz, Gernot ; Leeb, Robert ; Tangermann, Michael ; Hohne, Johannes ; Kubler, Andrea ; Cincotti, Febo ; Mattia, Donatella ; Rupp, Rudiger ; Muller, Klaus-Robert ; Del R Millan, Jose

  • Author_Institution
    Lab. of Brain-Comput. Interfaces, Graz Univ. of Technol., Graz, Austria
  • Volume
    103
  • Issue
    6
  • fYear
    2015
  • fDate
    Jun-15
  • Firstpage
    926
  • Lastpage
    943
  • Abstract
    In their early days, brain-computer interfaces (BCIs) were only considered as control channel for end users with severe motor impairments such as people in the locked-in state. But, thanks to the multidisciplinary progress achieved over the last decade, the range of BCI applications has been substantially enlarged. Indeed, today BCI technology cannot only translate brain signals directly into control signals, but also can combine such kind of artificial output with a natural muscle-based output. Thus, the integration of multiple biological signals for real-time interaction holds the promise to enhance a much larger population than originally thought end users with preserved residual functions who could benefit from new generations of assistive technologies. A BCI system that combines a BCI with other physiological or technical signals is known as hybrid BCI (hBCI). In this work, we review the work of a large scale integrated project funded by the European commission which was dedicated to develop practical hybrid BCIs and introduce them in various fields of applications. This article presents an hBCI framework, which was used in studies with nonimpaired as well as end users with motor impairments.
  • Keywords
    assisted living; brain-computer interfaces; electroencephalography; medical disorders; medical signal processing; muscle; prosthetics; BCI system; BCI technology; European commission; artificial output; assistive technologies; control channel; control signals; end users; hBCI framework; locked-in state; multidisciplinary progress; multiple biological signal integration; natural muscle-based output; noninvasive hybrid brain-computer interfaces; physiological signals; practical hybrid BCI; preserved residual functions; real-time interaction; severe motor impairments; technical signals; Assistive technology; Bayes methods; Brain-computer interfaces; Computer interfaces; Electroencephalography; Electromyography; Electronic mail; Neuroprosthesis; Assistive technology; communication; electroencephalogram; hybrid brain–computer interface (hBCI); hybrid brain???computer interface (hBCI); neuroprosthesis;
  • fLanguage
    English
  • Journal_Title
    Proceedings of the IEEE
  • Publisher
    ieee
  • ISSN
    0018-9219
  • Type

    jour

  • DOI
    10.1109/JPROC.2015.2411333
  • Filename
    7109824