@inbook{8eb7326fe87641e9ae407cd8ff0d3fec,
title = "Detecting Mental States by Machine Learning Techniques: The Berlin Brain–Computer Interface",
abstract = "The Berlin Brain-Computer InterfaceBerlinBrain-Computer Interface (BBCI) uses a machine learning approach to extract user-specific patterns from high-dimensional EEG-features optimized for revealing the user{\textquoteright}s mental state. Classical BCI applications are brain actuated tools for patients such as prostheses (see Section 4.1) or mental text entry systems ([1] and see [2–5] for an overview on BCI). In these applications, the BBCI uses natural motor skills of the users and specifically tailored pattern recognition algorithms for detecting the user{\textquoteright}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 [6] in the fMRI realm). While this field is still largely unexplored, two examples from our studies are exemplified in Sections 4.3 and 4.4.",
keywords = "Common Spatial Pattern, Error Index, Injuryspinal Cord, Motor Imagery, Readiness Potential",
author = "Benjamin Blankertz and Michael Tangermann and Carmen Vidaurre and Thorsten Dickhaus and Claudia Sannelli and Florin Popescu and Siamac Fazli and M{\'a}rton Dan{\'o}czy and Gabriel Curio and M{\"u}ller, \{Klaus Robert\}",
note = "Publisher Copyright: {\textcopyright} 2009, Springer-Verlag Berlin Heidelberg.",
year = "2009",
doi = "10.1007/978-3-642-02091-9\_7",
language = "English",
series = "Frontiers Collection",
publisher = "Springer VS",
pages = "113--135",
booktitle = "Frontiers Collection",
}