PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

The Berlin Brain-Computer Interface
Benjamin Blankertz, Michael Tangermann, Florin Popescu, Matthias Krauledat, Siamac Fazli, Marton Donaczy, Gabriel Curio and Klaus-Robert Müller
WCCI 2008 Plenary/Invited Lectures (LNCS) Volume 5050, pp. 79-101, 2008.


No Abstact, only Introduction: The Berlin Brain-Computer Interface (BBCI) uses a machine learning approach to extract subject-specific patterns from high-dimensional EEG-features optimized for revealing the user’s mental state. Classical BCI application are brain actuated tools for patients such as prostheses (see Section 4.1) or mental text entry systems ([2] and see [3,4,5,6] for an overview on BCI). In these applications the BBCI uses natural motor competences of the users and specifically tailored pattern recognition algorithms for detecting the user’s intent. But beyond rehabilitation, there is a wide range of possible applications in which BCI technology is used to monitor other mental states, often even covert ones (see also [7] in the fMRI realm). While this field is still largely unexplored, two examples from our studies are exemplified in Section 4.3 and 4.4.

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EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Brain Computer Interfaces
ID Code:5094
Deposited By:Benjamin Blankertz
Deposited On:24 March 2009