Deutsch
 
Hilfe Datenschutzhinweis Impressum
  DetailsucheBrowse

Datensatz

DATENSATZ AKTIONENEXPORT
Zur Ablage hinzufügen
  Lokale TagsFreigabegeschichteDetailsÜbersicht

Freigegeben
Zeitschriftenartikel

Effective network analysis in music listening based on electroencephalogram

MPG-Autoren
/persons/resource/persons212714

Teng,  Xiangbin       
Department of Neuroscience, Max Planck Institute for Empirical Aesthetics, Max Planck Society;
Department of Psychology, The Chinese University of Hong Kong;
SARNeuroscience Department, Max–Planck-Institute or Empirical Aesthetics;

/persons/resource/persons179725

Larrouy-Maestri,  Pauline       
Department of Music, Max Planck Institute for Empirical Aesthetics, Max Planck Society;
Department of Neuroscience, Max Planck Institute for Empirical Aesthetics, Max Planck Society;
Center for Language, Music, and Emotion (CLaME);

Externe Ressourcen
Es sind keine externen Ressourcen hinterlegt
Volltexte (beschränkter Zugriff)
Für Ihren IP-Bereich sind aktuell keine Volltexte freigegeben.
Volltexte (frei zugänglich)
Es sind keine frei zugänglichen Volltexte in PuRe verfügbar
Ergänzendes Material (frei zugänglich)
Es sind keine frei zugänglichen Ergänzenden Materialien verfügbar
Zitation

Tan, Y., Sun, Z., Teng, X., Larrouy-Maestri, P., Duan, F., & Aoki, S. (2024). Effective network analysis in music listening based on electroencephalogram. Computers and Electrical Engineering, 117: 109191. doi:10.1016/j.compeleceng.2024.109191.


Zitierlink: https://hdl.handle.net/21.11116/0000-000F-2C38-2
Zusammenfassung
Music is present in every culture and affects humans in numerous ways. Music-related technologies,
such as music generation, can extend the application scenarios of human–computer
interaction systems. Despite its important role in cognitive and social domains, the brain
networks involved in music listening remain unclear. To further explore the relationship
between music and brain networks, in this study, we analyzed the brain networks of 29
participants under different musical conditions based on electroencephalogram (EEG) signals.
Specifically, we utilized a public dataset that provided EEG signals of participants listening to
music under different rhythmic and processing conditions. After EEG source localization, we
selected 22 regions of interest (ROIs) that were relevant to music. The effective networks were
subsequently established, where the ROIs are regarded as nodes, and the Granger causality
relationships between nodes are considered as edges. We explored the differences among these
effective networks and analyzed them further based on graph theory. The results demonstrate
that different processing methods of music generate changes in the brain network. The results
indicate the crucial role of the inferior parietal lobe in information transmission. The findings
of this study provide new insights into the relationship between music and brain activity