title: The causal inference of cortical neural networks during music improvisations.
abstract: We present an EEG study of two music improvisation experiments. Professional musicians with high level of improvisation skills were asked to perform music either according to notes (composed music) or in improvisation. Each piece of music was performed in two different modes: strict mode and "let-go" mode. Synchronized EEG data was measured from both musicians and listeners. We used one of the most reliable causality measures: conditional Mutual Information from Mixed Embedding (MIME), to analyze directed correlations between different EEG channels, which was combined with network theory to construct both intra-brain and cross-brain networks. Differences were identified in intra-brain neural networks between composed music and improvisation and between strict mode and "let-go" mode. Particular brain regions such as frontal, parietal and temporal regions were found to play a key role in differentiating the brain activities between different playing conditions. By comparing the level of degree centralities in intra-brain neural networks, we found a difference between the response of musicians and the listeners when comparing the different playing conditions.
authors.affiliation: Department of Mathematics and Centre for Complexity Science, Imperial College London, London, United Kingdom.
authors.forename: Xiaogeng
authors.initials: X
authors.lastname: Wan
authors.affiliation: Brainmarker BV, Molenweg 15a, Gulpen, The Netherlands.
authors.forename: Björn
authors.initials: B
authors.lastname: Crüts
authors.affiliation: Department of Mathematics and Centre for Complexity Science, Imperial College London, London, United Kingdom.
authors.forename: Henrik Jeldtoft
authors.initials: HJ
authors.lastname: Jensen
mesh_terms: D001921:Brain / Q000502:physiology*; D004569:Electroencephalography; D006801:Humans; D009146:Music; D009415:Nerve Net / Q000502:physiology*; D012815:Signal Processing, Computer-Assisted