title: Music-induced emotions can be predicted from a combination of brain activity and acoustic features.
abstract: It is widely acknowledged that music can communicate and induce a wide range of emotions in the listener. However, music is a highly-complex audio signal composed of a wide range of complex time- and frequency-varying components. Additionally, music-induced emotions are known to differ greatly between listeners. Therefore, it is not immediately clear what emotions will be induced in a given individual by a piece of music. We attempt to predict the music-induced emotional response in a listener by measuring the activity in the listeners electroencephalogram (EEG). We combine these measures with acoustic descriptors of the music, an approach that allows us to consider music as a complex set of time-varying acoustic features, independently of any specific music theory. Regression models are found which allow us to predict the music-induced emotions of our participants with a correlation between the actual and predicted responses of up to r=0.234,p<0.001. This regression fit suggests that over 20% of the variance of the participant's music induced emotions can be predicted by their neural activity and the properties of the music. Given the large amount of noise, non-stationarity, and non-linearity in both EEG and music, this is an encouraging result. Additionally, the combination of measures of brain activity and acoustic features describing the music played to our participants allows us to predict music-induced emotions with significantly higher accuracies than either feature type alone (p<0.01).
authors.affiliation: Brain Embodiment Lab, School of Systems Engineering, University of Reading, Reading, UK. Electronic address: i.daly@reading.ac.uk.
authors.forename: Ian
authors.initials: I
authors.lastname: Daly
authors.affiliation: Interdisciplinary Centre for Music Research, University of Plymouth, Plymouth, UK.
authors.forename: Duncan
authors.initials: D
authors.lastname: Williams
authors.affiliation: Brain Embodiment Lab, School of Systems Engineering, University of Reading, Reading, UK.
authors.forename: James
authors.initials: J
authors.lastname: Hallowell
authors.affiliation: Brain Embodiment Lab, School of Systems Engineering, University of Reading, Reading, UK.
authors.forename: Faustina
authors.initials: F
authors.lastname: Hwang
authors.affiliation: Interdisciplinary Centre for Music Research, University of Plymouth, Plymouth, UK.
authors.forename: Alexis
authors.initials: A
authors.lastname: Kirke
authors.affiliation: Brain Embodiment Lab, School of Systems Engineering, University of Reading, Reading, UK.
authors.forename: Asad
authors.initials: A
authors.lastname: Malik
authors.affiliation: Brain Embodiment Lab, School of Systems Engineering, University of Reading, Reading, UK.
authors.forename: James
authors.initials: J
authors.lastname: Weaver
authors.affiliation: Interdisciplinary Centre for Music Research, University of Plymouth, Plymouth, UK.
authors.forename: Eduardo
authors.initials: E
authors.lastname: Miranda
authors.affiliation: Brain Embodiment Lab, School of Systems Engineering, University of Reading, Reading, UK.
authors.forename: Slawomir J
authors.initials: SJ
authors.lastname: Nasuto
mesh_terms: D000161:Acoustic Stimulation; D000293:Adolescent; D000328:Adult; D000368:Aged; D001307:Auditory Perception / Q000502:physiology*; D001921:Brain / Q000502:physiology*; D001931:Brain Mapping; D004569:Electroencephalography; D004644:Emotions / Q000502:physiology*; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D009146:Music / Q000523:psychology*; D055815:Young Adult
keywords: Acoustic features; Affective state prediction; EEG; Machine learning; Music