PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Detecting Mental States by Machine Learning Techniques: The Berlin Brain-Computer Interface
Benjamin Blankertz, M Tangermann, C Vidaurre, T Dickhaus, C Sannelli, F Popescu, S Fazli, M Danócy, G Curio and Klaus-Robert Müller
In: Brain-Computer Interfaces (Revolutionizing Human-Computer Interaction) (2010) Springer .

Abstract

The Berlin Brain-Computer Interface (BBCI) uses a machine learning approach 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 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’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.

EPrint Type:Book Section
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Brain Computer Interfaces
ID Code:8039
Deposited By:Benjamin Blankertz
Deposited On:17 March 2011