Brain–computer interfaces (BCIs) that integrate virtual reality with tactile feedback are increasingly relevant for neurorehabilitation in spinal cord injury (SCI). In our previous case study employing a BCI-based virtual reality neurorehabilitation protocol, a patient with complete T4 SCI experienced reduced pain and emergence of non-spastic lower limb movements after 10 sessions. However, it is still unclear whether these effects can be sustained, enhanced, and replicated, as well as the neural mechanisms that underlie them. The present report outlines the outcomes of extending the previous protocol with 24 more sessions (14 months, in total). Clinical, behavioral, and neurophysiological data were analyzed. The protocol maintained or reduced pain levels, increased self-reported quality of life, and was frequently associated with the appearance of non-spastic lower limb movements when the patient was engaged and not experiencing stressful events. Neural activity analysis revealed that changes in pain were encoded in the theta frequency band by the left frontal electrode F3. Examination of the lower limbs revealed alternating movements resembling a gait pattern. These results suggest that sustained use of this BCI protocol leads to enhanced quality of life, reduced and stable pain levels, and may result in the emergence of rhythmic patterns of lower limb muscle activity reminiscent of gait.

Real-time tactile feedback has been applied in exoskeleton-based neurorehabilitation, particularly in spinal cord injury, through Foot-to-Forearm Feedback (FFF) at ground contact. Its effects on healthy individuals across varied terrains remain less understood. This pilot study investigated seven healthy participants controlling a lower-limb exoskeleton under two conditions (with and without FFF) across five scenarios: Flat, M-Shape, A-Shape, V-Shape, and Random. Objective measures included step count, sacroiliac joint (SIJ) segment length, and SIJ angle. Subjective experience was assessed by overall preference and a Global Experience Index (GEI) derived from questionnaire ratings. Analyses showed no consistent differences in step count, SIJ length, or SIJ angle between Control and FFF. In three runs, technical issues occurred (e.g., a loose cable). Subjective data showed that five of seven participants (71.4%) preferred FFF, one reported no difference, and one preferred the Control condition. Scenario-specific analyses indicated a preference for FFF in Flat (5/7 = 71.4%), but a lower preference in Random (2/6 = 33.3%). GEI scores decreased under A-Shape and Random conditions, though FFF partially mitigated the reduction in A-Shape. These preliminary findings suggest that FFF may influence subjective experience in healthy users, but confirmation will require larger samples and further investigation across scenarios.

This paper investigates the changes in sensory neural activity during exoskeleton control. Exoskeletons are becoming reliable tools for neurorehabilitation, as recent studies have shown that their use enhances neural plasticity. However, the specific neural correlates associated with exoskeleton control have not yet been described in detail. Therefore, in this pilot study, our aim was to investigate the effects of different pavement textures on the neural signals of participants (n = 5) while controlling a lower limb ExoAtlet^®-powered exoskeleton. Subjects were instructed to walk on various types of pavements, including a flat surface, carpet, foam, and rubber circles, both with and without the exoskeleton. This setup resulted in eight different experimental conditions for classification (i.e., Exoskeleton/No Exoskeleton in one of four different pavements). Four-minute Electroencephalography (EEG) signals were recorded in each condition: (i) the power of the signals was compared for electrodes C3 and C4 across different conditions (Exoskeleton/No Exoskeleton on different pavements), and (ii) the signals were classified using four models: the linear support vector machine (L-SVM), the K-nearest neighbor algorithm (KNN), linear discriminant analysis (LDA), and the artificial neural network (ANN). the results of power analysis showed increases and decreases in power within the delta frequency bands in electrodes C3 and C4 across the various conditions. The results of comparison between classifiers revealed that LDA exhibited the highest performance with an accuracy of 85.71%. These findings support the notion that the sensory processing of pavement textures during exoskeleton control is associated with changes in the delta band of the C3 and C4 electrodes. From the results, it is concluded that the use of classifiers, such as LDA, allow for a better offline classification of different textures in EEG signals, with and without exoskeleton control, than the analysis of power in different frequency bands.