The ever increasing biocompatibility and pervasive nature of wearable and implantable devices demand novel sustainable solutions to realize their connectivity, which can impact broad application scenarios such as in the defense, biomedicine, and entertainment fields. Where wireless electromagnetic communications are facing challenges such as device miniaturization, energy scarcity, limited range, and possibility of interception, solutions not only inspired but also based on natural communication means might result into valid alternatives. In this paper, a communication paradigm where digital information is propagated through the nervous system is proposed and analyzed on the basis of achievable information rates. In particular, this paradigm is based on an analytical framework where the response of a system based on haptic (tactile) information transmission and ElectroEncephaloGraphy (EEG)-based reception is modeled and characterized. Computational neuroscience models of the somatosensory signal representation in the brain, coupled with models of the generation and propagation of somatosensory stimulation from skin mechanoreceptors, are employed in this paper to provide a proof-of-concept evaluation of achievable performance in encoding information bits into tactile stimulation, and decoding them from the recorded brain activity. Based on these models, the system is simulated and the resulting data are utilized to train a Support Vector Machine (SVM) classifier, which is finally used to provide a proof-of-concept validation of the system performance in terms of information rates against bit error probability at the reception.