The study of neural correlates of motor execution is commonly performed by means of event-related processing of electroencephalographic (EEG) recordings, in which each event refers to a standardized, repeatable movement. Some authors have proposed a valuable single-parameter method, the Event-Related Synchronization and Desynchronization (ERS/ERD) approach, for the identification of motor-related power modulation in each EEG frequency band. Under evolving experimental conditions (such as learning or adaptation), though, the repetition of a motor scheme becomes time-variant, and the employment of single-parameter descriptors no longer represents the optimal choice. This occurrence is typically found in motor learning and adaptation studies. In this work we compared the performance of the ERS/ERD method with the multi-parametric Hilbert Huang Transform (HHT). Results confirmed the statistically significant equivalence of the two methods in providing indexes of neural synchronization and desynchronization. Moreover, HHT allowed the tracking of frequency shifts in the alpha and beta EEG bands. The two methods were tested on an EEG dataset recorded during a motor adaptation test.