A Pilot Study of Neuroaesthetics Based on the Analysis of Electroencephalographic Connectivity Networks in the Visualization of Different Dance Choreography Styles

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Abstract

Neuroaesthetics allows us to understand how the brain works in different artistic languages and, therefore, to broaden the knowledge of our aesthetic judgments. The present pilot study is an interdisciplinary work that aims to differentiate different aesthetic dance choreography styles and to demonstrate the influence of training, learning, enculturation and familiarization of these styles on their brain perception by means of neurophysiological measurements of EEG signals and neural connectivity network analysis techniques. To this end, EEGs of non-expert dancers are recorded while viewing two fragments (film clips) of classical and modern dance and during other control conditions. Measures of functional connectivity between recorded regions are obtained from phase synchronization measurements between pairs of EEG signals in each EEG frequency band (FB). The responses of each FB are evaluated from indices obtained from models of EEG connectivity networks -graphs and connectomes- constructed from graph theory and network-based statistics (NBS) in a global and local context. Thus, significant alterations -in some of the indices- are observed between different contrasts and conditions in certain areas and specific EEG connections that depend on the EEG frequency band under consideration. These first results, therefore, suggest the usefulness of this neuroaesthetic experimental paradigm. On the other hand, these neuroaesthetic procedures may be of special interest in biomedicine because they provide knowledge about different languages that can be applied in therapies and treatments.

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González, A., Meléndez-Gallardo, J., & Gonzalez, J. J. (2023). A Pilot Study of Neuroaesthetics Based on the Analysis of Electroencephalographic Connectivity Networks in the Visualization of Different Dance Choreography Styles. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 13920 LNBI, pp. 297–310). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-34960-7_21

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