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

Recent studies show that the dynamics of electrophysiological functional connectivity is attracting more and more interest since it is considered as a better representation of functional brain networks than static network analysis. It is believed that the dynamic electrophysiological brain networks with specific frequency modes, transiently form and dissolve to support ongoing cognitive function during continuous task performance. Here, we propose a novel method based on tensor component analysis (TCA), to characterize the spatial, temporal, and spectral signatures of dynamic electrophysiological brain networks in electroencephalography (EEG) data recorded during free music-listening. A three-way tensor containing time-frequency phase-coupling between pairs of parcellated brain regions is constructed. Nonnegative CANDECOMP/PARAFAC (CP) decomposition is then applied to extract three interconnected, low-dimensional descriptions of data including temporal, spectral, and spatial connection factors. Musical features are also extracted from stimuli using acoustic feature extraction. Correlation analysis is then conducted between temporal courses of musical features and TCA components to examine the modulation of brain patterns. We derive several brain networks with distinct spectral modes (described by TCA components) significantly modulated by musical features, including higher-order cognitive, sensorimotor, and auditory networks. The results demonstrate that brain networks during music listening in EEG are well characterized by TCA components, with spatial patterns of oscillatory phase-synchronization in specific spectral modes. The proposed method provides evidence for the time-frequency dynamics of brain networks during free music listening through TCA, which allows us to better understand the reorganization of electrophysiological networks.