In this study, we propose a methodological strategy to analyze electromyographic (EMG) signals recorded from rats subjected to a neurotoxic model of Parkinson’s disease (PD), with the aim of evaluating whether different experimental conditions can be distinguished based on intrinsic features of the signals. Continuous EMG recordings from the biceps femoris muscle were obtained during spontaneous locomotion and decomposed using the Noise-Assisted Multivariate Empirical Mode Decomposition (NA-MEMD) algorithm. The Hilbert transform was applied to the resulting intrinsic mode functions (IMFs) to extract their instantaneous frequency and energy characteristics. These variables were subjected to multivariate statistical analysis (MANOVA), followed by a Gaussian Mixture Model (GMM) to assess clustering. The Adjusted Rand Index (ARI) was used as a metric to quantify separability. The results revealed statistically significant differences between experimental groups across multiple modes, with maximum ARI values observed in intermediate modes (up to 0.30), suggesting that these components may contain functionally relevant information. This approach enabled the characterization of muscular patterns affected by neurodegeneration in a bidimensional energy–frequency space. Future studies will aim to evaluate the temporal evolution of these clusters and to further clarify the physiological and/or functional origins of the EMG alterations induced by neurodegeneration in the PD animal model.