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

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.

Deriving Electrophysiological Brain Network Connectivity via Tensor Component Analysis During Freely Listening to Music

“…Thus, the forming of these connectivity patterns is plausible to understand semantics expressed by music and induce related emotion during music listening. For the neural oscillations, previous studies reported that cortical rhythm activity in the Beta range is related to behavioral performance during music listening and associated with predicting the upcoming note events [12], [60], which confirms our results that the frontal high-order networks with a high coherence in the Beta range emerged during music listening. In addition, the Delta-specific high-order cognitive network (Row I of Fig.…”

“…These networks involved in auditory areas ( Fig. 4) showed beta-specific modes, which play an important function in music perception in agreement with previous studies [12], [17], [60].…”

“…A 50 Hz notch filter was applied to remove powerline interference. High-pass and low-pass filters with 2 Hz and 35 Hz cutoff were then used since our previous investigation of the frequency range uncovered that no useful information was observed in higher frequencies [12], [42]. The data were finally down-sampled to 256 Hz.…”

Sustaining Attention for a Prolonged Duration Affects Dynamic Organizations of Frequency-Specific Functional Connectivity

“…In general, the multi-mode data were stacked or concatenated to facilitate two-way processing methods (e.g., independent component analysis (ICA) and principal component analysis (PCA)) for extracting interested brain activities (Bernat et al 2005;Cong et al 2010;Dien 2010;Tenke and Kayser 2005;Vigário and Oja 2008;Zhu et al 2020b). The procedures of stacking and concatenating inevitably lost potential interaction information (Cong et al 2015(Cong et al 2013a.…”

Identifying Oscillatory Hyperconnectivity and Hypoconnectivity Networks in Major Depression Using Coupled Tensor Decomposition

“…Considering the temporal and spectral dynamics of spatial couplings (e.g., functional connectivity) for multiple participants in a cognitive task, a multi-way dataset structure is naturally formed. This multi-dimensional nature points to the adoption of tensor decomposition models instead of matrix decomposition models which normally fold some dimensions and ignore the hidden interactions across different modes [23], [25]- [28]. Canonical Polyadic (CP) decomposition is derived in terms of the sum of multiple rank-one tensors, and each rank-one tensor represents the covariation of the corresponding components from each mode [29], [30].…”