Identifying music-induced emotions from EEG for use in brain-computer music interfacing

Daly, I, Malik, A, Weaver, J, Hwang, F, Nasuto, SJ, Williams, DAH ORCID: https://orcid.org/0000-0003-4793-8330, Kirke, A and Miranda, E 2015, Identifying music-induced emotions from EEG for use in brain-computer music interfacing , in: 2015 International Conference on Affective Computing and Intelligent Interaction (ACII), 21st-24th September 2015, Xi'an, China.

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Abstract

Brain-computer music interfaces (BCMI) provide a method to modulate an individuals affective state via the selection or generation of music according to their current affective state. Potential applications of such systems may include entertainment of therapeutic applications. We outline a proposed design for such a BCMI and seek a method for automatically differentiating different music induced affective states. Band-power features are explored for use in automatically identifying music-induced affective states. Additionally, a linear discriminant analysis classifier and a support vector machine are evaluated with respect to their ability to classify music induced affective states from the electroencephalogram recorded during a BCMI calibration task. Accuracies of up to 79.5% (p <; 0.001) are achieved with the support vector machine.

Item Type: Conference or Workshop Item (Paper)
Schools: Schools > School of Computing, Science and Engineering
Journal or Publication Title: 2015 International Conference on Affective Computing and Intelligent Interaction (ACII)
Publisher: IEEE
ISBN: 9781479999538
ISSN: 2156-8111
Funders: Engineering and Physical Sciences Research Council (EPSRC)
Depositing User: USIR Admin
Date Deposited: 12 Dec 2019 14:33
Last Modified: 29 Jan 2020 14:19
URI: http://usir.salford.ac.uk/id/eprint/55640

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