Linking biomechanical model dynamics and neural complexity: Permutation entropy approaches to motor control

This study presents a novel complexity-based framework integrating neural and biomechanical perspectives to assess motor asymmetry and brain lateralization. Using principles from non-linear dynamics and information theory, we model motor output by treating the biomechanical scaling exponent α from a mean velocity-displacement power law found in human movement data: V∝Dα as a time-varying marker of control strategy we are proposing a quantitative framework to approach degrees of freedom in motor control. Participants (right-handed, left-handed, and ambidextrous) performed writing and drawing tasks with both hands. Biomechanical data were decomposed into sub-movements, generating α time series whose temporal diversity was quantified using Permutation Entropy (PE). PE analysis was applied to EEG signals as well, in order to assess neural dynamics patterns related to hand-dominance across time scales. Analysis showed higher PE in dominant-hand movement data, reflecting richer motor variability related to more degrees of freedom. EEG analysis indicates that right-handers displayed shorter time reaction differences between hands during handwriting. Time series patterns suggest that right-handers are not genuinely more hand-dependent, but less familiar with left hand use. By linking time-evolving motor output and neural control through entropy measures, this approach offers sensitive tools to measure asymmetries in biological signals, essential for studying motor control.