Detailed Information

Cited 0 time in webofscience Cited 0 time in scopus
Metadata Downloads

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

Authors
Mueller-Putz, GernotLeeb, RobertTangermann, MichaelHoehne, JohannesKuebler, AndreaCincotti, FeboMattia, DonatellaRupp, RuedigerMueller, Klaus-RobertMillan, Jose del R.
Issue Date
Jun-2015
Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Keywords
Assistive technology; communication; electroencephalogram; hybrid brain-computer interface (hBCI); neuroprosthesis
Citation
PROCEEDINGS OF THE IEEE, v.103, no.6, pp.926 - 943
Indexed
SCIE
SCOPUS
Journal Title
PROCEEDINGS OF THE IEEE
Volume
103
Number
6
Start Page
926
End Page
943
URI
https://scholar.korea.ac.kr/handle/2021.sw.korea/93381
DOI
10.1109/JPROC.2015.2411333
ISSN
0018-9219
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.
Files in This Item
There are no files associated with this item.
Appears in
Collections
Graduate School > Department of Artificial Intelligence > 1. Journal Articles

qrcode

Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.

Altmetrics

Total Views & Downloads

BROWSE