This study presents a method based on empirical mode decomposition (EMD) and a spatial template-based matching approach to extract sensorimotor oscillatory activities from multi-channel magnetoencephalographic (MEG) measurements during right index finger lifting. The longitudinal gradiometer of the sensor unit which presents most prominent SEF was selected on which each single-trial recording was decomposed into a set of intrinsic mode functions (IMFs). The correlation between each IMF of the selected channel and raw data on other channels were created and represented as a spatial map. The sensorimotor-related IMFs with corresponding correlational spatial map exhibiting large values on primary sensorimotor area (SMI) were selected via spatial-template matching process. Trial-specific alpha and beta bands were determined in sensorimotor-related oscillatory activities using a two-spectrum comparison between the spectra obtained from baseline period (-4 to -3 s) and movement-onset period (-0.5 to 0.5 s). Sensorimotor-related oscillatory activities were filtered within the trial-specific frequency bands to resolve task-related oscillatory activities. Results demonstrated that the optimal phase and amplitude information were preserved not only for alpha suppression (event-related desynchronization) and beta rebound (event-related synchronization) but also for profound analysis of subtle dynamics across trials. The retention of high SNR in the extracted oscillatory activities allow various methods of source estimation that can be applied to study the intricate brain dynamics of motor control mechanisms. The present study enables the possibility of investigating cortical pathophysiology of movement disorder on a trial-by-trial basis which also permits an effective alternative for participants or patients who can not endure lengthy procedures or are incapable of sustaining long experiments.