NATURALISTIC MUSIC STABILIZES ALPHA-BAND PHASE DYNAMICS IN HUMAN ELECTROENCEPHALOGRAM

The temporal evolution of neuronal phase and its predictability in naturalistic settings, such as music listening, remains underexplored in the alpha-band (8-13 Hz) of electroencephalogram (EEG) studies. In this study, we propose a novel framework for quantifying short-horizon phase predictability and phase stability by modeling phase prediction errors using a linear approximation that captures the diffusion-like behavior of alpha-band oscillations. The framework was evaluated on two music-EEG datasets. The first dataset consists of EEG data from 13 healthy participants listening to live Indian classical music (ICM), comprising two ragas and relaxation conditions. The second dataset, the naturalistic music EEG dataset - tempo (NMED-T), consisted of EEG recordings from 20 participants listening to 10 songs. For both datasets, individual alpha frequencies (IAF) were computed to obtain subject-specific band-pass signals. Instantaneous phase was extracted from the analytic signal using the Hilbert transform and predicted over short horizons (Δ = 50, 100, 150, 200 ms) using instantaneous frequency estimates. Mean absolute phase prediction errors were modeled linearly to estimate the diffusion slope, providing a measure of phase stability. The results from the ICM dataset showed consistently higher phase prediction errors during the relaxation condition than during both music conditions. The diffusion slopes were lower during music, suggesting slower accumulation of phase prediction error and greater short-horizon phase stability. Paired t-tests revealed statistically significant differences between music and relaxation for both phase prediction errors and diffusion slopes. Comparative results were observed on the NMED-T dataset. These findings demonstrate that naturalistic music listening is associated with more stable alpha-band phase dynamics, providing a quantitative framework for assessing neural oscillatory stability in continuous EEG signals.