Recent studies have suggested that cortical gamma-oscillations are tightly linked with various forms of physiological activity. In the present study, the dynamic changes of intracranially recorded median-nerve somatosensory-evoked potentials (SEPs) and somatosensory-induced gamma-oscillations were animated on a three-dimensional MR image, and the temporal and spatial characteristics of these activities were analysed in 10 children being evaluated for epilepsy surgery. Visual and quantitative assessments revealed that short-latency SEPs and somatosensory-induced gamma-oscillations predominantly involved the post-central gyrus and less intensely involved the pre-central gyrus and the anterior parietal lobule. Formation of a dipole of N20 peak with opposite polarities across the central sulcus was well delineated in animation movies. High-frequency (100-250 Hz) somatosensory-induced gamma-oscillations emerged in the post-central gyrus at 13.6-17.5 ms after median-nerve stimulation, gradually slowed down in frequency around and below 100 Hz, and progressively involved the neighbouring areas. A substantial proportion of somatosensory-induced gamma-oscillations was initially phase-locked and the proportion of a non-phase-locked component gradually increased over time. The primary motor hand areas proven by cortical stimulation frequently coincided with the sites showing the largest N20 peak and the largest somatosensory-induced gamma oscillations. "In vivo" animation of SEPs and somatosensory-induced gamma oscillations both may be utilized to localize the primary sensory-motor hand area in pre-surgical evaluation. The dipole on SEPs is consistent with the previously accepted notion that the cortices along the central sulcus are activated. The high-frequency somatosensory-induced gamma-oscillations in the post-central gyrus may represent the initial neural processing for external somatosensory stimuli, whereas the subsequent lower-frequency oscillations might represent the reafferent cortical activity occurring in larger cortical networks.