Jieren Xie

Music can effectively influence human emotions, with different melodies and rhythms eliciting varying emotional responses. Among these, tempo is one of the most important parameters affecting emotions. This study explores the impact of music tempo on emotional states and the associated brain functional networks. A total of 26 participants without any history of neurological or psychiatric disorders and music training took part in the experiment, using classical piano music clips at different tempi (56, 106, 156 bpm) as stimuli. The study was conducted using emotional scales and electroencephalogram (EEG) analysis. The results showed that the valence level of emotions significantly increased with music tempo, while the arousal level exhibited a “V” shape relationship. EEG analysis revealed significant changes in brainwave signals across different frequency bands under different tempi. For instance, slow tempo induced higher Theta and Alpha power in the frontal region, while fast tempo increased Beta and Gamma band power. Moreover, fast tempo enhanced the average connectivity strength in the frontal, temporal, and occipital regions, and increased phase synchrony value (PLV) between the frontal and parietal regions. However, slow tempo improves PLV between the occipital and parietal regions. The findings of this study elucidate the effects of music tempo on the brain functional networks related to emotion regulation, providing a theoretical basis for music-assisted diagnosis and treatment of mood disorders. Furthermore, these results suggest potential applications in emotion robotics, emotion-based human-computer interaction, and emotion-based intelligent control.

This paper presents a novel approach to the phase space reconstruction technique, fractional-order phase space reconstruction (FOSS), which generalizes the traditional integer-order derivative-based method. By leveraging fractional derivatives, FOSS offers a novel perspective for understanding complex time series, revealing unique properties not captured by conventional methods. We further develop the multi-span transition entropy component method (MTECM-FOSS), an advanced complexity measurement technique that builds upon FOSS. MTECM-FOSS decomposes complexity into intra-sample and inter-sample components, providing a more comprehensive understanding of the dynamics in multivariate data. In simulated data, we observe that lower fractional orders can effectively filter out random noise. Time series with diverse long- and short-term memory patterns exhibit distinct extremities at different fractional orders. In practical applications, MTECM-FOSS exhibits competitive or superior classification performance compared to state-of-the-art algorithms when using fewer features, indicating its potential for engineering tasks.