The Causal Inference of Cortical Neural Networks during Music Improvisations

Xiaogeng Wan, Björn Crütz and Henrik Jeldtoft Jensen

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 intrabrain and cross-brain networks. Differences were identified in intrabrain 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.

Reference: PLOS ONE | DOI:10.1371/journal.pone.
0112776 December 9, 2014