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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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Organizations and authors

University of Helsinki

Poikonen Hanna

Huotilainen Minna

University of Jyväskylä

Toiviainen Petri Orcid -palvelun logo

Ristaniemi Tapani Orcid -palvelun logo

Zhu Yongjie Orcid -palvelun logo

Cong Fengyu

Publication type

Publication format

Article

Parent publication type

Journal

Article type

Original article

Audience

Scientific

Peer-reviewed

Peer-Reviewed

MINEDU's publication type classification code

A1 Journal article (refereed), original research

Publication channel information

Parent publication name

Brain Topography

Volume

33

Issue

3

Pages

289-302

​Publication forum

52630

​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