Bioelectronic data and biomechatronic tools applied to the somatosensory system for diagnosis and prognosis in neurorehabilitation

Touch is the first sense to develop in the womb, allowing us to receive information about external environments. The somatosensory system continuously provides sensory feedback to the central nervous system, enabling the recognition of objects, the discrimination of textures, and the exchange of social cues. Moreover, sensory neurons project primitive sensations such as pain and temperature from the skin to the brain, allowing the body to detect harmful stimuli and carry out protective reflexes. In addition to its well-recognized role in proprioception, nociception, temperature, and discrimination, touch also presents an affective aspect. In light of the role of touch in human life, a comprehensive exploration of the somatosensory system is gaining interest in the scientific community. In this thesis, the development of a mechatronic platform for exploring touch assumed a crucial role in carrying out this investigation. Similarly, the exploration of both simple and complex tactile stimuli aided in characterizing the encoding processes involved in touch perception through psychophysical tests. Moreover, the employment of innovative techniques, such as microneurostimulation, and the analysis of bioelectronic data, such as microneurography or electroencephalography, significantly increase the knowledge about the physiology of touch, from the specific role of single mechanoreceptor to the corresponding brain activation. In this work, the insights gained into the tactile perception are used for exploring methods to restore sensation in individuals with upper limb amputation. Building on fundamental research of the somatosensory system, there is significant potential for translation into clinical applications. The bioelectronic data play a crucial role in the diagnosis and the prognosis of different pathological conditions, helping in the identification of the more appropriate rehabilitation pathway. In this study, the electromyographic data analysis for automatic diagnosis in Chronic Dysimmune Polyneuropathies is explored, emphasizing the clinical interpretation of outcomes and identifying key neurophysiological variables able to enhance the interpretability for clinicians. Moreover, the electroencephalographic data analysis associated with Surface Cutaneous Stimulation and Near-Nerve Stimulation for prognosis in patients with Disorder of Consciousness is examined. The aim was to understand the role of somatosensory stimulation in identifying the residual brain activity and consciousness level in this complex population, identifying EEG-based predictive biomarkers for the definition of consciousness recovery. On the whole, these insights contribute to the development of Clinical Decision Support Tools aimed at improving diagnostic and prognostic precision and patient care. Overall, this PhD thesis integrates findings from diverse methodologies and technologies to advance our understanding of the physiology of the somatosensory system and its role in neurorehabilitation. It underscores the transformative potential of bioelectronic and biomechatronic approaches in enhancing diagnostic accuracy and prognostic capabilities across various neurological conditions.