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An ECoG-Based BCI Based on Auditory Attention to Natural Speech

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Brain-Computer Interface Research

Part of the book series: SpringerBriefs in Electrical and Computer Engineering ((BRIEFSELECTRIC))

Abstract

People affected by severe neuro-degenerative diseases (e.g., late-stage amyotrophic lateral sclerosis (ALS) or locked-in syndrome) eventually lose all muscular control and are no longer able to gesture or speak. For this population, an auditory BCI is one of only a few remaining means of communication. All currently used auditory BCIs require a relatively artificial mapping between a stimulus and a communication output. This mapping is cumbersome to learn and use. Recent studies suggest electrocorticographic (ECoG) signals in the gamma band (i.e., 70–170 Hz) can be used to infer the identity of auditory speech stimuli, effectively removing the need to learn such an artificial mapping. However, BCI systems that use this physiological mechanism for communication purposes have not yet been described. In this study, we explore this possibility by implementing a BCI2000-based real-time system that uses ECoG signals to identify the attended speaker.

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Acknowledgements

This work was supported by the NIH (EB006356 (GS), EB00856 (GS) and EB018783 (GS)), the US Army Research Office (W911NF-07-1-0415 (GS), W911NF-08-1-0216 (GS) and W911NF-14-1-0440 (GS)) and Fondazione Neurone.

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Authors and Affiliations

  1. New York State Department of Health, Center for Adapt Neurotech, Wadsworth Center, Albany, NY, USA

    Peter Brunner, Karen Dijkstra, William G. Coon, Jürgen Mellinger, Anthony L. Ritaccio & Gerwin Schalk

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  1. Peter Brunner
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  2. Karen Dijkstra
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  3. William G. Coon
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  4. Jürgen Mellinger
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  5. Anthony L. Ritaccio
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  6. Gerwin Schalk
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Correspondence to Gerwin Schalk .

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  1. g.tec Guger Technologies OG , 4521 Schiedlberg, Austria

    Christoph Guger

  2. g.tec Guger Technologies OG, 4521 Schiedlberg, Austria

    Brendan Allison

  3. Biosciences and Informatics, Keio University Biosciences and Informatics, 223-8522 Yokohama, Kazagawa, Japan

    Junichi Ushiba

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Brunner, P., Dijkstra, K., Coon, W.G., Mellinger, J., Ritaccio, A.L., Schalk, G. (2017). An ECoG-Based BCI Based on Auditory Attention to Natural Speech. In: Guger, C., Allison, B., Ushiba, J. (eds) Brain-Computer Interface Research. SpringerBriefs in Electrical and Computer Engineering. Springer, Cham. https://doi.org/10.1007/978-3-319-57132-4_2

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  • DOI: https://doi.org/10.1007/978-3-319-57132-4_2

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  • Online ISBN: 978-3-319-57132-4

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