Affective brain–computer music interfacing

Ian Daly; Duncan Williams; Alexis Kirke; James Weaver; Asad Malik; Faustina Hwang; Eduardo Miranda; Slawomir J Nasuto

doi:10.1088/1741-2560/13/4/046022

Affective brain–computer music interfacing

Ian Daly, Duncan Williams, Alexis Kirke, James Weaver, Asad Malik, Faustina Hwang, Eduardo Miranda, Slawomir J Nasuto

Computer Science

Research output: Contribution to journal › Article › peer-review

Abstract

Objective . We aim to develop and evaluate an affective brain–computer music interface (aBCMI) for modulating the affective states of its users. Approach . An aBCMI is constructed to detect a user's current affective state and attempt to modulate it in order to achieve specific objectives (for example, making the user calmer or happier) by playing music which is generated according to a specific affective target by an algorithmic music composition system and a case-based reasoning system. The system is trained and tested in a longitudinal study on a population of eight healthy participants, with each participant returning for multiple sessions. Main results . The final online aBCMI is able to detect its users current affective states with classification accuracies of up to 65% and modulate its user's affective states significantly above chance level. Our system represents one of the first demonstrations of an online aBCMI that is able to accurately detect and respond to user's affective states. Possible applications include use in music therapy and entertainment.

Original language	English
Pages (from-to)	046022
Number of pages	15
Journal	Journal of Neural Engineering
Volume	13
Issue number	4
DOIs	https://doi.org/10.1088/1741-2560/13/4/046022
Publication status	Published - 11 Jul 2016

Access to Document

10.1088/1741-2560/13/4/046022

http://hdl.handle.net/10026.1/6517

Cite this

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title = "Affective brain–computer music interfacing",

abstract = "Objective . We aim to develop and evaluate an affective brain–computer music interface (aBCMI) for modulating the affective states of its users. Approach . An aBCMI is constructed to detect a user's current affective state and attempt to modulate it in order to achieve specific objectives (for example, making the user calmer or happier) by playing music which is generated according to a specific affective target by an algorithmic music composition system and a case-based reasoning system. The system is trained and tested in a longitudinal study on a population of eight healthy participants, with each participant returning for multiple sessions. Main results . The final online aBCMI is able to detect its users current affective states with classification accuracies of up to 65% and modulate its user's affective states significantly above chance level. Our system represents one of the first demonstrations of an online aBCMI that is able to accurately detect and respond to user's affective states. Possible applications include use in music therapy and entertainment.",

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T1 - Affective brain–computer music interfacing

AU - Daly, Ian

AU - Williams, Duncan

AU - Kirke, Alexis

AU - Weaver, James

AU - Malik, Asad

AU - Hwang, Faustina

AU - Miranda, Eduardo

AU - Nasuto, Slawomir J

PY - 2016/7/11

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N2 - Objective . We aim to develop and evaluate an affective brain–computer music interface (aBCMI) for modulating the affective states of its users. Approach . An aBCMI is constructed to detect a user's current affective state and attempt to modulate it in order to achieve specific objectives (for example, making the user calmer or happier) by playing music which is generated according to a specific affective target by an algorithmic music composition system and a case-based reasoning system. The system is trained and tested in a longitudinal study on a population of eight healthy participants, with each participant returning for multiple sessions. Main results . The final online aBCMI is able to detect its users current affective states with classification accuracies of up to 65% and modulate its user's affective states significantly above chance level. Our system represents one of the first demonstrations of an online aBCMI that is able to accurately detect and respond to user's affective states. Possible applications include use in music therapy and entertainment.

AB - Objective . We aim to develop and evaluate an affective brain–computer music interface (aBCMI) for modulating the affective states of its users. Approach . An aBCMI is constructed to detect a user's current affective state and attempt to modulate it in order to achieve specific objectives (for example, making the user calmer or happier) by playing music which is generated according to a specific affective target by an algorithmic music composition system and a case-based reasoning system. The system is trained and tested in a longitudinal study on a population of eight healthy participants, with each participant returning for multiple sessions. Main results . The final online aBCMI is able to detect its users current affective states with classification accuracies of up to 65% and modulate its user's affective states significantly above chance level. Our system represents one of the first demonstrations of an online aBCMI that is able to accurately detect and respond to user's affective states. Possible applications include use in music therapy and entertainment.

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