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International Symposium on Computer Music Multidisciplinary Research

CMMR 2015: Music, Mind, and Embodiment pp 132–145Cite as

The Hybrid Brain Computer Music Interface - Integrating Brainwave Detection Methods for Extended Control in Musical Performance Systems

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Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9617))

Abstract

In the pursuit of creative interfaces for music making brain-computer interfacing (BCI) control methods offer limited usability especially in terms of providing simultaneous yet independent input controls. This article outlines the development of the hybrid brain-computer music interface (BCMI) and presents the work undertaken towards the design, implementation, and combined BCI control methods employed. Two active (steady state visually evoked potential (SSVEP) and motor imagery (MI)) and one passive (affective response) control methods are integrated in a new BCMI system, which can be applied to a range of music-making activities. This paper also briefly outlines the design of bespoke, customisable and modular SSVEP stimuli and feedback units. As a demonstration of the hybrid-BCMI, the performance piece A Stark Mind illustrates how such a system is in for live performance. The piece uses the brainwave control methods to generate and control a visual score for an ensemble of musicians to perform.

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Notes

  1. 1.

    https://www.ableton.com/en/push/.

  2. 2.

    http://www.gtec.at/.

  3. 3.

    http://uk.mathworks.com/products/simulink/.

  4. 4.

    http://puredata.info/.

  5. 5.

    http://resolume.com/.

  6. 6.

    http://www.arduino.cc/.

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Acknowledgments

The authors gratefully acknowledge the support of a doctoral scholarship awarded by Plymouth University.

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Authors and Affiliations

  1. Interdisciplinary Centre for Computer Music Research, Plymouth University, Plymouth, UK

    Joel Eaton & Eduardo R. Miranda

Authors
  1. Joel Eaton
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  2. Eduardo R. Miranda
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Corresponding author

Correspondence to Joel Eaton .

Editor information

Editors and Affiliations

  1. CNRS - LMA, Marseille Cedex 20, France

    Richard Kronland-Martinet

  2. CNRS - LMA, Marseille, France

    Mitsuko Aramaki

  3. CNRS - LMA, Marseille, France

    Sølvi Ystad

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Eaton, J., Miranda, E.R. (2016). The Hybrid Brain Computer Music Interface - Integrating Brainwave Detection Methods for Extended Control in Musical Performance Systems. In: Kronland-Martinet, R., Aramaki, M., Ystad, S. (eds) Music, Mind, and Embodiment. CMMR 2015. Lecture Notes in Computer Science(), vol 9617. Springer, Cham. https://doi.org/10.1007/978-3-319-46282-0_8

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  • DOI: https://doi.org/10.1007/978-3-319-46282-0_8

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-46281-3

  • Online ISBN: 978-3-319-46282-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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