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The Berlin Brain-Computer Interface (BBCI) - towards a new communication channel for online control in gaming applications

 
: Krepki, R.; Blankertz, B.; Curio, G.; Müller, K.-R.

:

Ip, H.H.S.:
Special issue on distributed adaptation representation and processing of multimedia information : Visual information systems, VIS 2003, Miami, FL
Berlin: Springer, 2007 (Multimedia tools and applications 33.2007, Nr.1)
pp.73-90
International Conference on Visual Information Systems (VIS) <2003, Miami/Fla.>
English
Conference Paper, Journal Article
Fraunhofer FIRST ()

Abstract
The investigation of innovative Human-Computer Interfaces (HCI) provides a challenge for future multimedia research and development. Brain-Computer Interfaces (BCI) exploit the ability of human communication and control bypassing the classical neuromuscular communication channels. In general, BCIs offer a possibility of communication for people with severe neuromuscular disorders, such as Amyotrophic Lateral Sclerosis (ALS) or spinal cord injury. Beyond medical applications, a BCI conjunction with exciting multimedia applications, e. g., a dexterity game, could define a new level of control possibilities also for healthy customers decoding information directly from the user's brain, as reflected in electroencephalographic (EEG) signals which are recorded non-invasively from user's scalp. This contribution introduces the Berlin Brain-Computer Interface (BBCI) and presents setups where the user is provided with intuitive control strategies in plausible gaming applications that use biofeedback. Yet at its beginning, BBCI thus adds a new dimension in multimedia research by offering the user an additional and independent communication channel based on brain activity only. First successful experiments already yielded inspiring proofs-of-concept. A diversity of multimedia application models, say computer games, and their specific intuitive control strategies, as well as various Virtual Reality (VR) scenarios are now open for BCI research aiming at a further speed up of user adaptation and increase of learning success and transfer bit rates.

: http://publica.fraunhofer.de/documents/N-59390.html