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A Tutorial on EEG Signal-processing Techniques for Mental-state Recognition in Brain–Computer Interfaces

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Guide to Brain-Computer Music Interfacing

Abstract

This chapter presents an introductory overview and a tutorial of signal-processing techniques that can be used to recognize mental states from electroencephalographic (EEG) signals in brain–computer interfaces. More particularly, this chapter presents how to extract relevant and robust spectral, spatial, and temporal information from noisy EEG signals (e.g., band-power features, spatial filters such as common spatial patterns or xDAWN, etc.), as well as a few classification algorithms (e.g., linear discriminant analysis) used to classify this information into a class of mental state. It also briefly touches on alternative, but currently less used approaches. The overall objective of this chapter is to provide the reader with practical knowledge about how to analyze EEG signals as well as to stress the key points to understand when performing such an analysis.

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Notes

  1. 1.

    Note that this was estimated before SVM were invented and that SVM are generally less sensitive—although not completely immune—to this curse-of-dimensionality.

  2. 2.

    BCI competitions are contests to evaluate the best signal processing and classification algorithms on given brain signals data sets. See http://www.bbci.de/competition/ for more info.

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  1. Inria Bordeaux Sud-Ouest/LaBRI, 200 Avenue de la Vieille Tour, 33405, Talence Cedex, France

    Fabien Lotte

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  1. Fabien Lotte
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  1. Interdisciplinary Centre for Computer Music Research (ICCMR), Plymouth University, Plymouth, United Kingdom

    Eduardo Reck Miranda

  2. Immersion, Bordeaux, France

    Julien Castet

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Lotte, F. (2014). A Tutorial on EEG Signal-processing Techniques for Mental-state Recognition in Brain–Computer Interfaces. In: Miranda, E., Castet, J. (eds) Guide to Brain-Computer Music Interfacing. Springer, London. https://doi.org/10.1007/978-1-4471-6584-2_7

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