The Causal Inference of Cortical Neural Networks during Music Improvisations
File(s)PLoS ONE_9_12_2014 (2).pdf (1.43 MB)
Published version
Author(s)
Jensen, HJ
Wan, X
Crüts, B
Type
Journal Article
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.
Date Issued
2014-12-09
Citation
PLOS One, 2014, 9 (12)
ISSN
1932-6203
Publisher
Public Library of Science
Journal / Book Title
PLOS One
Volume
9
Issue
12
Copyright Statement
© 2014 Wan et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
License URL
Identifier
http://www.plosone.org/article/info:doi/10.1371/journal.pone.0112776
e112776
Subjects
Bioacoustics
Centrality
Electroencephalography
Prefrontal cortex
Neural networks
Network analysis
Music cognition
Functional magnetic resonance imaging
Publication Status
Published
Article Number
e112776