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).

Music-induced changes in functional cerebral asymmetries

“…This finding is in line with previous studies showing that the left hemisphere has a better temporal resolution (e.g., Tallal et al, 1993) and that changes in musical tempo entrain activity in the left motor cortex (Daly et al, 2015). Therefore, it is possible that changes in lateralization after listening to Mozart found in the current study is due to greater left hemisphere activity resulting from the fast tempo and greater temporal variation of Mozart's piano sonata.…”

Review and Classification of Emotion Recognition Based on EEG Brain-Computer Interface System Research: A Systematic Review

“…In these studies, brain signals were recorded using an EEG headset while the subject listens to music [44,53,58,100,110,112,115,116,151,154,190,205,216,220,222,235,276,279]. Moreover, the subjects' emotions were recognized as displayed by EEG signals.…”

A Review on EEG Signals Based Emotion Recognition

“…By using good and strong stimulation, emotion recognition is more likely to be performed with better results and higher accuracy. There are some types of stimulation as follow: pictures, 12-43 video clips, [44][45][46][47][48][49][50][51][52][53][54][55][56][57][58][59][60] music, [61][62][63][64][65][66][67][68][69][70][71][72][73][74][75][76] memories, 77 self-induction, 78,80,81 environment elicitation like light, humidity and temperature, 79 games, 82 etc. Some ways of eliciting emotions and some induced emotions are listed in Table 1.…”