Exploring Frequency-Dependent Brain Networks from Ongoing EEG Using Spatial ICA During Music Listening

Brain Topography, vol. 33, pp. 289–302

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.

Authors 8

1. Yongjie Zhu

   Dalian University of Technology · University of Jyväskylä

   Affiliation as printed

   Faculty of Information Technology, University of Jyväskylä, Jyväskylä, 40014, Finland

   School of Biomedical Engineering, Faculty of Electronic and Electrical Engineering, Dalian University of Technology, Dalian, 116024, China

2. Chi Zhang

   Dalian University of Technology

   Affiliation as printed

   School of Biomedical Engineering, Faculty of Electronic and Electrical Engineering, Dalian University of Technology, Dalian, 116024, China

3. Hanna Poikonen

   ETH Zurich

   Affiliation as printed

   Institute of Learning Sciences and Higher Education, ETH Zürich, Zürich, Switzerland

4. Petri Toiviainen

   University of Jyväskylä

   Affiliation as printed

   Department of Music, Art and Culture Studies, University of Jyväskylä, Jyväskylä, 40014, Finland

5. Minna Huotilainen

   University of Helsinki

   Affiliation as printed

   CICERO Learning Network and Cognitive Brain Research Unit, Faculty of Educational Sciences, University of Helsinki, Helsinki, 00014, Finland

6. Klaus Mathiak Aachen Department of Psychiatry, Psychotherapy and Psychosomatics Medical Faculty

   RWTH Aachen University

   Affiliation as printed

   Department of Psychiatry, Psychotherapy and Psychosomatics, Medical Faculty, RWTH Aachen, Pauwelsstraße 30, Aachen, 52074, Germany

7. Tapani Ristaniemi

   University of Jyväskylä

   Affiliation as printed

   Faculty of Information Technology, University of Jyväskylä, Jyväskylä, 40014, Finland

8. Fengyu Cong corresponding

   Dalian University of Technology · University of Jyväskylä

   Affiliation as printed

   Faculty of Information Technology, University of Jyväskylä, Jyväskylä, 40014, Finland. cong@dlut.edu.cn

   School of Biomedical Engineering, Faculty of Electronic and Electrical Engineering, Dalian University of Technology, Dalian, 116024, China. cong@dlut.edu.cn

   Faculty of Information Technology, University of Jyväskylä, Jyväskylä, 40014, Finland

   School of Biomedical Engineering, Faculty of Electronic and Electrical Engineering, Dalian University of Technology, Dalian, 116024, China