PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Forschen an einer neuen Schnittstelle zum Gehirn: Das Berliner Brain-Computer Interface
Klaus-Robert Müller, Benjamin Blankertz, Michael Tangermann and Gabriel Curio
Nova Acta Leopoldina Volume 110, Number 377, pp. 235-257, 2011.

Abstract

The Berlin Brain-Computer Interface (BBCI) uses machine learning approaches to extract user-specific patterns from high-dimensional EEG-features optimized for revealing the user's mental state. Classical BCI applications are brain-actuated tools for paralyzed patients such as prostheses or mental text entry. In these applications, the BBCI uses natural motor skills of the users and specifically tailored pattern recognition algorithms for detecting the user's intentions. Beyond rehabilitation, there is a wide range of novel applications in which BCI technology is used to monitor other mental states, even covert ones.

EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Brain Computer Interfaces
ID Code:9413
Deposited By:Klaus-Robert Müller
Deposited On:16 March 2012