A Statistical Inference Framework for Understanding Music-Related Brain Activity

Following the success in Music Information Retrieval (MIR), research is now steering towards understanding the relationship existing between brain activity and the music stimuli causing it. To this end, a new MIR topic has emerged, namely Music Imagery Information Retrieval, with its main scope being to bridge the gap existing between music stimuli and its respective brain activity. In this paper, the encephalographic modality was chosen to capture brain activity as it is more widely available since of-the-shelf devices recording such responses are already affordable unlike more expensive brain imaging techniques. After defining three tasks assessing different aspects of the specific problem (stimuli identification, group, and meter classification), we present a common method to address them, which explores the temporal evolution of the acquired signals. In more detail, we rely on the parameters of linear time-invariant models extracted out of electroencephalographic responses to heterogeneous music stimuli. Subsequently, the probability density function of such parameters is estimated by hidden Markov models taking into account their succession in time. We report encouraging classification rates in the abovementioned tasks, suggesting the existence of an underlying relationship between music stimuli and their electroencephalographic responses.