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Detecting Mental States by Machine Learning Techniques: The Berlin Brain–Computer Interface

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Brain-Computer Interfaces

Part of the book series: The Frontiers Collection ((FRONTCOLL))

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.

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Notes

  1. 1.

    The \(r^2\) value (squared biserial correlation coefficient) is a measure for how much of the variance in one variable is explained by the variance in a second variable, i.e., it is a measure for how good a single feature is in predicting the label.

  2. 2.

    This study was performed in cooperation with the Daimler AG. For further information, we refer to [55].

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Acknowledgment

We are very grateful to Nicole Krämer (TU Berlin) for pointing us to the analytic solution of the optimal shrinkage parameter for regularized linear discriminant analysis, see Section 2.2. The studies were partly supported by the Bundesministerium für Bildung und Forschung (BMBF), Fkz 01IB001A/B, 01GQ0850, by the German Science Foundation (DFG, contract MU 987/3-1), by the European Union’s Marie Curie Excellence Team project MEXT-CT-2004-014194, entitled “Brain2Robot” and by their IST Programme under the PASCAL Network of Excellence, ICT-216886. This publication only reflects the authors’ views. We thank our coauthors for allowing us to use published material from [7, 48, 52, 55–57, 60].

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Authors and Affiliations

  1. Machine Learning Laboratory, Berlin Institute of Technology, Berlin, Germany

    Benjamin Blankertz, Michael Tangermann, Carmen Vidaurre, Thorsten Dickhaus, Claudia Sannelli, Siamac Fazli, Márton Danóczy & Klaus-Robert Müller

  2. Fraunhofer FIRST (IDA), Berlin, Germany

    Benjamin Blankertz & Florin Popescu

  3. Campus Benjamin Franklin, Charité University Medicine Berlin, Berlin, Germany

    Gabriel Curio

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  1. Otto Bock HealthCare GmbH, Max-Näder-Str. 15, Duderstadt, 37115, Germany

    Bernhard Graimann

  2. Institut für Semantische Datenanalyse/, Knowledge Discovery, Technische Universität Graz, Krenngasse 37, Graz, 8010, Austria

    Gert Pfurtscheller

  3. Inst. Semantische Datenanalyse, Brain-Computer Interface Labor (BCI), TU Graz, Krenngasse 37/III, Graz, 8010, Austria

    Brendan Allison

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Blankertz, B. et al. (2009). Detecting Mental States by Machine Learning Techniques: The Berlin Brain–Computer Interface. In: Graimann, B., Pfurtscheller, G., Allison, B. (eds) Brain-Computer Interfaces. The Frontiers Collection. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02091-9_7

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