Spatiotemporal properties of motor cortex neuronal activity have been extensively studied, with the interpretations of voluntary movement mechanisms being the key issue. Two competing models, representational and dynamical, explain the conversion of neuronal activity into limb movements. The dynamical model uses the joint PCA method, which holistically characterizes neuronal oscillations by maximizing data rotational features. Different interpretations tried to explain the presence of rotational dynamics in neuronal patterns (Fig. 1a, c). Yet, their exact nature remains poorly understood.Here we employed a purely data-driven approach to free our analysis from representational or dynamical model assumptions. We focused instead on the neuronal-population properties generating rotational dynamics. We analyzed several datasets known to exhibit rotational dynamics. Using a novel complex-valued measure, the gyration number, and an explicit mathematical model to quantify rotation strength we identify the parameters contributing to the rotations (Fig. 1b).We found that rotational dynamics across datasets is consistently explained by a travelling wave pattern. The introduction of the gyration number, alongside with mathematical model, enabled accurate exploration of the underlying dynamics. Overall, our results indicate that rotational dynamics and travelling waves represent the same phenomenon. This conclusion bridges the previous studies that considered neuronal dynamics and travelling waves in isolation.Thus, our study for the first time provides compelling evidence that rotational dynamics are fundamentally linked to travelling wave patterns. This work not only explains rotational dynamics but also advances our understanding of the complex neuronal populations signal processing, opening new avenues for the motor cortex functions exploration.