Background and Objectives: The recovery of stroke patients with severe impairment is usually poor and limited and, unfortunately, under-investigated in clinical studies. In order to support neuroplasticity and modulate motor recovery, Cerebrolysin combined with rehabilitation treatment has proven effective in the acute stroke phase in moderate to severe motor impairment. The aim of this study was to determine the efficacy of extended poststroke rehabilitation combined with Cerebrolysin on upper limb motor recovery in subacute stroke patients with severe upper limb motor impairment. Materials and Methods: A randomized, double-blind, placebo-controlled study was conducted. Sixty patients at the early stage of severe sub-acute stroke who fulfilled all eligibility criteria were randomly assigned to the Cerebrolysin group or placebo group (𝑛 = 30 each). Both groups, after conducting three weeks of conventional rehabilitation treatment five days per week, continued to perform conventional rehabilitation treatment three times per week until 90 days of rehabilitation treatment. The primary outcome measure was the Action Research Arm Test (ARAT), and the secondary outcomes were the Fugl-Meyer Assessment-Upper Extremity (FMA-UE) motor score, Barthel index (BI), and the National Institutes of Health Stroke Scale (NIHSS). The outcome data were evaluated before, after three weeks of treatment, and on the 90th day of rehabilitation treatment, and compared within groups and between the two groups. There were no adverse events. Results: Both groups showed a significant improvement (p < 0.001) over time in BI, FMA-UE, ARAT, and NIHSS scores. Patients receiving Cerebrolysin showed more significant improvement in post-stroke upper limb motor impairment and functioning compared to the placebo group after only three weeks, and the trend was maintained after 90 days of follow up. Conclusion: Cerebrolysin delivered in the early subacute post-stroke phase added to extended conventional rehabilitation treatment is beneficial and improves motor functional recovery in patients with severe motor impairment, especially on the paretic upper extremity.

This study investigates the feasibility of a novel brain–computer interface (BCI) device designed for sensory training following stroke. The BCI system administers electrotactile stimuli to the user’s forearm, mirroring classical sensory training interventions. Concurrently, selective attention tasks are employed to modulate electrophysiological brain responses (somatosensory event-related potentials—sERPs), reflecting cortical excitability in related sensorimotor areas. The BCI identifies attention-induced changes in the brain’s reactions to stimulation in an online manner. The study protocol assesses the feasibility of online binary classification of selective attention focus in ten subacute stroke patients. Each experimental session includes a BCI training phase for data collection and classifier training, followed by a BCI test phase to evaluate online classification of selective tactile attention based on sERP. During online classification tests, patients complete 20 repetitions of selective attention tasks with feedback on attention focus recognition. Using a single electroencephalographic channel, attention classification accuracy ranges from 70% to 100% across all patients. The significance of this novel BCI paradigm lies in its ability to quantitatively measure selective tactile attention resources throughout the therapy session, introducing a top-down approach to classical sensory training interventions based on repeated neuromuscular electrical stimulation.

Traditional tactile brain–computer interfaces (BCIs), particularly those based on steady-state somatosensory–evoked potentials, face challenges such as lower accuracy, reduced bit rates, and the need for spatially distant stimulation points. In contrast, using transient electrical stimuli offers a promising alternative for generating tactile BCI control signals: somatosensory event-related potentials (sERPs). This study aimed to optimize the performance of a novel electrotactile BCI by employing advanced feature extraction and machine learning techniques on sERP signals for the classification of users’ selective tactile attention. The experimental protocol involved ten healthy subjects performing a tactile attention task, with EEG signals recorded from five EEG channels over the sensory–motor cortex. We employed sequential forward selection (SFS) of features from temporal sERP waveforms of all EEG channels. We systematically tested classification performance using machine learning algorithms, including logistic regression, k-nearest neighbors, support vector machines, random forests, and artificial neural networks. We explored the effects of the number of stimuli required to obtain sERP features for classification and their influence on accuracy and information transfer rate. Our approach indicated significant improvements in classification accuracy compared to previous studies. We demonstrated that the number of stimuli for sERP generation can be reduced while increasing the information transfer rate without a statistically significant decrease in classification accuracy. In the case of the support vector machine classifier, we achieved a mean accuracy over 90% for 10 electrical stimuli, while for 6 stimuli, the accuracy decreased by less than 7%, and the information transfer rate increased by 60%. This research advances methods for tactile BCI control based on event-related potentials. This work is significant since tactile stimulation is an understudied modality for BCI control, and electrically induced sERPs are the least studied control signals in reactive BCIs. Exploring and optimizing the parameters of sERP elicitation, as well as feature extraction and classification methods, is crucial for addressing the accuracy versus speed trade-off in various assistive BCI applications where the tactile modality may have added value.

Background: Stroke recovery is influenced not only by clinical but also sociodemographic factors (SDFs). However, data on how variables such as age, sex, marital status, education, and employment status affect rehabilitation outcomes remain limited, particularly in structured inpatient settings. This study aimed to analyze the impact of key SDFs on functional recovery after stroke. Methods: A retrospective cohort of 289 stroke patients undergoing structured inpatient rehabilitation was analyzed. Functional status was assessed at admission, after three weeks, and at discharge using five standardized outcomes: gait speed (primary outcome), Barthel Index, Berg Balance Scale, Action Research Arm Test, and Ashworth scale. Repeated measures ANOVA and multivariable logistic regression were used to evaluate within-subject changes and associations with SDFs. Results: The cohort consisted predominantly of middle-aged to older adults (58% female, 62% married, 60% retired, 60% with primary education or less). Most patients (88%) had ischemic strokes of moderate severity. Significant improvements were observed across all functional measures. Employed, married, younger, and male patients achieved better outcomes. Interaction models indicated that older and female patients with moderate stroke severity demonstrated greater improvement than younger and male counterparts with milder strokes. Mean gait speed increased by +0.32 m/s, exceeding the minimal clinically important difference (MCID) of 0.16 m/s. Conclusions: Age, sex, marital status, education, and employment status are relevant predictors of stroke rehabilitation outcomes. These findings emphasize the importance of incorporating sociodemographic profiles into individualized rehabilitation planning to optimize functional recovery and reduce disparities among stroke survivors.

Sophisticated hand movements are essential for daily activities, but central neurological impairments often compromise them. Since full recovery through conventional physiotherapy is rare, assistance is crucial. While neural implants show promise, clinical use remains distant, urging immediate assistive alternatives. Current exoskeletons and neurostimulation garments lack sufficient motor support and sensory feedback, limiting dexterity. We developed a neurorobotic system combining portable exoskeletons with targeted neurostimulation via custom-made e-sleeve and tested it in 14 individuals with central neural injuries. We provide evidence of restored finger mobility and tactile perception, even in patients with clinically complete sensory loss, by recruiting residual peripheral pathways. Eight participants completed functional assessments, in which they exploited neurostimulation to improve grasp precision and enhance strength. This enabled manipulation of fragile and cumbersome objects, essential for everyday activities. Personalized assistive technologies have clinical potential to promote independence and support the reintegration of people with neurological impairments into society.

Introduction/Aim: In progressive forms of MS, the frequency of pain increases as the disease progresses affecting patients' functional abilities and making the disease much more complex. We conducted a cross-sectional study to examine the association of pain with walking speed and functional abilities in patients who suffer from progressive forms of multiple sclerosis. Material and methods: The cross-sectional study was conducted at the Clinic for rehabilitation "Dr Miroslav Zotović" in the period from January 2020 to May 2023. The research included 55 patients with PPMS and SPMS consecutively admitted to this Clinic for rehabilitation. Demographic and socio-epidemiological data and disease-related data were collected from all the patients. Pain intensity was assessed using Numeric Rating Scale (NRS). Since all patients experienced spasticity, pain was also assessed using the Pain/Discomfort (PD) subscale of the Multiple Sclerosis Spasticity Scale 88 (MSSS-88). The subjective perception of gait impairment was assessed using a subscale of the same questionnaire, MSSS 88, related to walking, namely the Walk (WL). Walking speed was measured by The Timed 25 Foot Walk (T25FW). The functional assessment and all questionnaires were completed in the morning hours over a 24-hour period from the day patients were admitted to rehabilitation. Results: There is a significant strong correlation between WL and P/D (rho=0. 770; p<0.001) and between WL and NRS (rho=0. 825 p<0.001). There is a statistically significant moderate negative correlation between T25FW and NRS pain (p<0.001). There is no statistically significant correlation between T25FW and pain intensity measured by PD (p=0.033). There is a statistically significant correlation between EDSS and pain intensity (NRS p=0.002; PD p=0.006) either. Conclusion: The results of this research indicated a significant negative impact of pain on walking speed and functional disability.