Detecting mental states by machine learning techniques: The Berlin Brain-Computer Interface
Benjamin Blankertz, Michael Tangermann, Carmen Vidaurre, Thorsten Dickhaus, Claudia Sanelli, Florin Popescu, Siamac Fazli, Marton Danoczy, Gabriel Curio and Klaus-Robert Müller
In: Non-Invasive and Invasive Brain-Computer Interfaces The Frontiers Collection . (2009) Springer .

Abstract

The Berlin Brain-Computer Interface (BBCI) uses a machine learning approach to extract user-specic 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 [25] for an overview on BCI). In these applications, the BBCI uses natural motor skills of the users and specically 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 eld is still largely unexplored, two examples from our studies are exemplied in Sections 4.3 and 4.4.

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