Background: Developing Brain–Computer Interfaces (BCIs) for children with severe neuromotor disabilities remains challenging due to developmental neurophysiology and the high cognitive demands of motor imagery. Hybrid BCIs integrating electroencephalography (EEG) and electrooculography (EOG) can overcome these limitations by combining continuous motor-related control with reliable discrete commands. This multiple-case study investigated the feasibility of a hybrid EEG–EOG BCI integrated with a virtual reality (VR) powered wheelchair simulator for mobility training. Methods: Six participants (mean age: 13.00 ± 3.52 years; 1 female, 5 males) with severe neuromotor disabilities (i.e., Central Core Myopathy, LAMA2 related Congenital Muscular Dystrophy, Spinal Muscular Atrophy Type II, and Cerebral Palsy) completed repeated sessions using a self-paced BCI. The system combined continuous kinesthetic motor imagery (KMI) for steering and EOG-detected voluntary blinks for discrete commands. We evaluated online classification performance (accuracy, sensitivity, specificity), user workload (NASA-TLX), and simulator-related discomfort (MSAQ) under fully and semi-immersive VR conditions. Results: Across the selected session-level evaluations, mean online classification accuracy was 67%, increasing to 77% when considering only successful evaluations. Four of the six participants completed the experimental protocol, whereas two discontinued after T3 because sufficiently stable BCI control could not be achieved. NASA-TLX scores indicated increasing perceived workload but generally low frustration, while low MSAQ scores suggested good VR tolerability. No participants withdrew because of VR-related discomfort or adverse effects. Conclusions: This exploratory study provides preliminary evidence supporting the feasibility of hybrid EEG–EOG BCI control within a VR-based powered mobility simulator for children and adolescents with severe neuromotor disabilities. Although simulated navigation was feasible for four participants, substantial variability in BCI control highlights the need for further technological refinement and validation in larger cohorts.

Neurological disorders, such as Parkinson's disease and stroke, often lead to neurogenic dysphagia, a swallowing disorder that compromises nutrition and increases the risk of malnutrition, aspiration of food, and even death. Although instrumental assessments remain the gold standard for evaluating swallowing function, their invasiveness, costs, and reliance on specialized clinical facilities limit their routine use. In this scoping review, we provide an overview of emerging noninvasive wearable devices for assessing and monitoring dysphagia in neurological disorders. We highlight the most widely used sensing technologies, such as accelerometers, microphones, and surface electromyography, alone and in multimodal configurations, discuss their respective strengths and limitations, and outline the main challenges and opportunities for advancing this field. Particular attention is given to the need for standardized protocols, robust clinical validation, and the integration of artificial intelligence to enable scalable and precise assessment. We conclude by discussing how these technologies can be applied in clinical practice to support earlier diagnosis, continuous monitoring, and improved management of swallowing function in individuals with neurological disorders.

Background: Developing Brain–Computer Interfaces (BCIs) for children with severe neuromotor disabilities remains challenging due to developmental neurophysiology and the high cognitive demands of motor imagery. Hybrid BCIs integrating electroencephalography (EEG) and electrooculography (EOG) can overcome these limitations by combining continuous motor-related control with reliable discrete commands. This multiple-case study investigated the feasibility of a hybrid EEG–EOG BCI integrated with a virtual reality (VR) powered-wheelchair simulator for mobility training. Methods: Six participants (one child, five adolescents) with severe neuromotor disabilities completed repeated sessions using a self-paced BCI. The system combined continuous kinesthetic motor imagery (KMI) for steering and EOG-detected voluntary blinks for discrete commands. We evaluated online classification performance (accuracy, sensitivity, specificity), user workload (NASA-TLX), and simulator-related discomfort (MSAQ) under fully and semi-immersive VR conditions. Results: Despite expected inter-subject variability, most participants achieved functional online VR wheelchair control, reaching a mean accuracy of 77% in successful sessions with balanced sensitivity and specificity. NASA-TLX scores indicated moderate cognitive workload but consistently low frustration. Zero dropouts and low MSAQ scores demonstrated excellent protocol tolerability, high engagement, and minimal cybersickness across both VR modalities. Conclusions: This study demonstrates the feasibility of combining hybrid EEG–EOG BCI control with VR for pediatric powered mobility training. The framework enabled functional control with high usability and minimal adverse effects, supporting the clinical potential of hybrid BCI–VR systems as accessible assistive technologies and providing a foundation for larger clinical trials.

The analysis of speech in individuals with amyotrophic lateral sclerosis is a powerful tool to support clinicians in the assessment of bulbar dysfunction. However, current methods used in clinical practice consist of subjective evaluations or expensive instrumentation. This study investigates different approaches combining audio-visual analysis and machine learning to predict the speech impairment evaluation performed by clinicians. Using a small dataset of acoustic and kinematic features extracted from audio and video recordings of speech tasks, we trained and tested some regression models. The best performance was achieved using the extreme boosting machine regressor with multimodal features, which resulted in a root mean squared error of 0.93 on a scale ranging from 5 to 25. Results suggest that integrating audio-video analysis enhances speech impairment assessment, providing an objective tool for early detection and monitoring of bulbar dysfunction, also in home settings.