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Decoding auditory attention to instruments in polyphonic music using single-trial EEG classification

Treder, M. S., Purwins, H., Miklody, D., Sturm, I. and Blankertz, B. 2014. Decoding auditory attention to instruments in polyphonic music using single-trial EEG classification. Journal of Neural Engineering 11 (2) , 026009. 10.1088/1741-2560/11/2/026009

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Abstract

Objective. Polyphonic music (music consisting of several instruments playing in parallel) is an intuitive way of embedding multiple information streams. The different instruments in a musical piece form concurrent information streams that seamlessly integrate into a coherent and hedonistically appealing entity. Here, we explore polyphonic music as a novel stimulation approach for use in a brain–computer interface. Approach. In a multi-streamed oddball experiment, we had participants shift selective attention to one out of three different instruments in music audio clips. Each instrument formed an oddball stream with its own specific standard stimuli (a repetitive musical pattern) and oddballs (deviating musical pattern). Main results. Contrasting attended versus unattended instruments, ERP analysis shows subject- and instrument-specific responses including P300 and early auditory components. The attended instrument can be classified offline with a mean accuracy of 91% across 11 participants. Significance. This is a proof of concept that attention paid to a particular instrument in polyphonic music can be inferred from ongoing EEG, a finding that is potentially relevant for both brain–computer interface and music research.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Computer Science & Informatics
Publisher: IOP Publishing
ISSN: 1741-2560
Date of Acceptance: 16 January 2014
Last Modified: 19 Jul 2019 08:06
URI: http://orca.cf.ac.uk/id/eprint/115686

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