multiSyncPy

multiSyncPy provides computational methods to quantify synchronization and coordination in multivariate time-series across individuals, groups, teams, families, and multiple behavioral or physiological modalities for research on interpersonal and group-level coordination.

Key Features:

• Multivariate synchronization analysis: Provides metrics to assess synchronization across individuals, groups, teams, families, and multiple modalities.
• Symbolic Entropy: Measures the complexity and predictability of time-series using symbolic dynamics.
• Multidimensional Recurrence Quantification: Analyzes recurrence patterns and dynamical structures in multidimensional datasets.
• Coherence: Evaluates the degree of synchrony between signals.
• Cluster-Phase ‘Rho’ Metric: Assesses phase synchronization within clusters of oscillators.
• Kuramoto Order Parameter-Based Statistical Test: Provides a statistical framework to evaluate synchrony using the Kuramoto order parameter.
• Surrogation techniques: Implements two surrogation techniques to compare observed coordination dynamics against chance levels.
• Data applicability: Applicable to both synthetic and empirical time-series datasets.

Scientific Applications:

• Psychology: Investigating interpersonal synchrony, joint action, and social coordination.
• Neuroscience: Analyzing neural synchrony and coordinated brain activity across subjects or modalities.
• Social sciences: Studying coordination in groups, teams, and collective behavior.
• Interdisciplinary synchronization research: Supporting analyses of synchronization phenomena across behavioral and physiological modalities in ecologically valid datasets.

Methodology:

Implements symbolic entropy, multidimensional recurrence quantification, coherence, cluster-phase ‘Rho’, a Kuramoto order parameter-based statistical test, and two surrogation techniques for analysis of synthetic and empirical multivariate time-series.