Exploring Frequency-Dependent Brain Networks from Ongoing EEG Using Spatial ICA During Music Listening
Year of publication
2020
Authors
Zhu, Yongjie; Zhang, Chi; Poikonen, Hanna; Toiviainen, Petri; Huotilainen, Minna; Mathiak, Klaus; Ristaniemi, Tapani; Cong, Fengyu
Abstract
Recently, exploring brain activity based on functional networks during naturalistic stimuli especially music and video represents an attractive challenge because of the low signal-to-noise ratio in collected brain data. Although most efforts focusing on exploring the listening brain have been made through functional magnetic resonance imaging (fMRI), sensor-level electro- or magnetoencephalography (EEG/MEG) technique, little is known about how neural rhythms are involved in the brain network activity under naturalistic stimuli. This study exploited cortical oscillations through analysis of ongoing EEG and musical feature during freely listening to music. We used a data-driven method that combined music information retrieval with spatial Fourier Independent Components Analysis (spatial Fourier–ICA) to probe the interplay between the spatial profiles and the spectral patterns of the brain network emerging from music listening. Correlation analysis was performed between time courses of brain networks extracted from EEG data and musical feature time series extracted from music stimuli to derive the musical feature related oscillatory patterns in the listening brain. We found brain networks of musical feature processing were frequency-dependent. Musical feature time series, especially fluctuation centroid and key feature, were associated with an increased beta activation in the bilateral superior temporal gyrus. An increased alpha oscillation in the bilateral occipital cortex emerged during music listening, which was consistent with alpha functional suppression hypothesis in task-irrelevant regions. We also observed an increased delta–beta oscillatory activity in the prefrontal cortex associated with musical feature processing. In addition to these findings, the proposed method seems valuable for characterizing the large-scale frequency-dependent brain activity engaged in musical feature processing.
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Publication type
Publication format
Article
Parent publication type
Journal
Article type
Original article
Audience
ScientificPeer-reviewed
Peer-ReviewedMINEDU's publication type classification code
A1 Journal article (refereed), original researchPublication channel information
Journal
Parent publication name
Volume
33
Issue
3
Pages
289-302
ISSN
Publication forum
Publication forum level
1
Open access
Open access in the publisher’s service
Yes
Open access of publication channel
Partially open publication channel
License of the publisher’s version
CC BY
Self-archived
Yes
License of the self-archived publication
CC BY
Other information
Fields of science
Computer and information sciences; Neurosciences; Psychology; Other humanities
Keywords
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Publication country
United States
Internationality of the publisher
International
Language
English
International co-publication
Yes
Co-publication with a company
No
DOI
10.1007/s10548-020-00758-5
The publication is included in the Ministry of Education and Culture’s Publication data collection
Yes