Clinical conditions can result in the need to replace, restore, or enhance the sense of touch in the upper extremity to improve both the quality of sensory feedback and also to improve motor control. However, current evaluations on the efficacy of touch-based therapies are focused on changes of functional measures, not objective neurophysiological measurements to determine if, where, and to what extent neuroplasticity has occurred. To move toward a neurophysiological evaluation of the effects of touch-based therapies in clinical populations, the goal of this dissertation was to identify the neurophysiological mechanisms underlying touch-guided movement in an unimpaired population. Three separate but related studies were conducted to investigate the mechanisms of neuroplasticity using electroencephalography (EEG) that support the short-term learning of touch-guided movement. These mechanisms include cortical activation, functional coupling between distant brain regions, and neuronal excitability. The first study revealed the short-term persistence of cortical activation and coupling patterns initiated by a touch- or vision-guided trajectory tracking task despite switching sensory modalities in a subsequent task. The second study identified increased functional coupling between the left temporal area and other cortical regions as a biomarker of learning for touch-guided trajectory tracking. The third study showed that the use of movement to explore a tactile environment leads to greater efficiency in motor preparation compared to passive tactile exposure, however both types of experience improved cutaneous tactile sensitivity. The identification of these neural correlates may be of relevance to future studies in neurorehabilitation. The presence of practice order effects may help inform the order to tasks during therapies in the future (i.e., practicing tactile-guided tasks before attempting similar visually-guided tasks). Additionally, changes in functional coupling and neuronal excitability should be explored as potential biomarkers to quantify neuroplasticity in therapeutic interventions and as a feedback parameter in brain-computer interfaces (BCI) to present biofeedback to patients to further induce neuroplasticity.