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Contemporary Approaches to Music BCI Using P300 Event Related Potentials

Chapter

Abstract

This chapter is intended as a tutorial for those interested in exploring the use of P300 event related potentials (ERPs) in the creation of brain computer music interfaces (BCMIs). It also includes results of research in refining digital signal processing (DSP) approaches and models of interaction using low-cost, portable BCIs. We will look at a range of designs for BCMIs using ERP techniques. These include the P300 Composer, the P300 Scale Player, the P300 DJ and the P300 Algorithmic Improviser. These designs have all been used in both research and performance, and are described in such a way that they should be reproducible by other researchers given the methods and guidelines indicated. The chapter is not intended to be exhaustive in terms of its neuroscientific detail, although the systems and approaches documented here have been reproduced by many labs, which should be an indication of their quality. Instead, what follows is a basic introduction to what ERPs are, what the P300 is, and how it can be applied in the development of these BCMI designs. This description of ERPs is not intended to be exhaustive, and at best should be thought of as an illustration designed to allow the reader to begin to understand how such approaches can be used for new instrument development. In this way, this chapter is intended to be indicative of what can be achieved, and to encourage others to think of BCMI problems in ways that focus on the measurement and understanding of signals that reveal aspects of human cognition. With this in mind, towards the end of the chapter we look at the results of our most recent research in the area of P300 BCIs that may have an impact on the usability of future BCI systems for music.

Keywords

Oddball Task P300 Detection Musical Interaction P300 BCIs P300 Event Related Potential 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag London 2014

Authors and Affiliations

  1. 1.Embodied Audiovisual Interaction Group (EAVI), Goldsmiths Digital Studios, Department of ComputingGoldsmiths CollegeLondonUK

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