Editorial: Biosignal processing and computational methods to enhance sensory motor neuroprosthetics.

Abstract

Neuroprosthetics is an interdisciplinary field of study that comprises neuroscience, computer science, physiology, and biomedical engineering. Each of these areas contributes to finally enhance the functionality of neural prostheses for the substitution or restoration of motor, sensory or cognitive funtions that might have been damaged as a result of an injury or a disease. For example, heart pace makers and cochlear implants substitute the functions performed by the heart and the ear by emulating biosignals with artificial pulses. These approaches require reliable bio-signal processing and computational methods to provide functional augmentation of damaged senses and actions. This Research Topic aims at bringing together recent advances in sensory motor neuroprosthetics. This issue includes research articles in all relevant areas of neuroprosthetics: (1) biosignal processing, especially of Electromyography (EMG) and Electroencephalography (EEG) signals, and other modalities of biofeedback information, (2) computational methods for modeling parts of the sensorimotor system, (3) control strategies for delivering the optimal therapy, (4) therapeutic systems aiming at providing solutions for specific pathological motor disorders, (5) man-machine interfaces, such as a brain-computer interface (BCI), as an interaction modality between the patient and the neuroprostheses. One challenging issue in motor prosthetics is the variability in the clinical presentation of patients, who show a variety of neurological disorders and physiological conditions. In order to improve neuroprosthetic performance beyond the current limited use, reliable bio-signal processing for extracting the intended neural information is needed (Farina et al., 2014). This information extraction stage can also be based on a modeling approach. Personalized neuroprosthetics with bio-signal feedback (Hayashibe et al., 2011; Borton et al., 2013; Li et al., 2014) could be a break-through toward intelligent neuroprosthetics. Combining different engineering techniques, such as in a hybrid approach (Del-Ama et al., 2014), is essential to expand the range of technological applications for wider patient populations. Recent advances of BCI are also relevant in this field to enable patients to transmit their intention of movement and its usage both for functional and rehabilitative purposes. This Research Topic comprises original research activities in different levels of maturity ranging from hypothesis and poof-of-concept (Dutta et al., 2014; Grahn et al., 2014b) to systems already tested with some patients. It also contains a variety of approaches from computational method to experimental studies. Following the recent intensive developments of advanced BCI systems (Leeb et al., 2015; Muller-Putz et al., 2015), many contributions in this Research Topic are provided in the field of BCI, both with the aim of functional replacement and for neurorehabilitation. We overview those contributions for each category.