Introduction. Non-invasive brain–computer interfaces (BCIs) enable feedback motor imagery [MI] training in neurological patients to support their motor rehabilitation. Nowadays, the use of BCIs based on functional near-infrared spectroscopy (fNIRS) for motor rehabilitation is yet to be investigated. Objective: To evaluate the potential fNIRS BCI use in hand MI training for comprehensive post-stroke rehabilitation. Materials and methods. This pilot study included clinically stable patients with mild-to-moderate post-stroke hand paresis. In addition to the standard rehabilitation, the patients underwent 10 nine-minute MI fNIRS BCI training sessions. To evaluate the quality of fNIRS BCI control, we assessed the percentage of time during which the classifier accurately detected patient's mental state. We scored the hand function using the Action Research Arm Test (ARAT) and the Fugl-Meyer Assessment (FMA). Results. The study included 5 patients at 1 day to 12 months of stroke. All the participants completed the study. All study participants achieved BCI control rates higher than random (41–68%). While three patients demonstrated the clinically significant improvements in their ARAT scores, one of them also showed an improvement in the FMA score. All the participants reported experiencing drowsiness during training. Conclusions. Post-stroke patients can operate the fNIRS BCI system under investigation. We suggest adjusting the feedback system, extending the duration of training, and incorporating functional electromyostimulation to enhance training effectiveness.

Motor imagery training under the control of a brain-computer interface (BCI) facilitates motor recovery after stroke. The efficacy of BCI based on electroencephalography (EEG-BCI) has been confirmed by several meta-analyses, but a more convenient and noise-resistant method of near-infrared spectroscopy in the BCI circuit (NIRS-BCI) has been practically unexamined; comparisons of the two types of BCI have not been performed.Objective: to compare the control accuracy and clinical efficacy of NIRS-BCI and EEG-IMC in post-stroke rehabilitation.Material and methods. The NIRS-BCI group consisted of patients from an uncontrolled study (n=15; 9 men and 6 women; age – 59.0 [49.0; 70.0] years; stroke duration – 7.0 [2.0; 10.0] months; upper limb paresis – 47.0 [35.0; 54.0] points on the Fugl-Meyer Assessment for motor function evaluation of the upper limb – FM-UL). The EEG-IMC group was formed from the main group of the randomized controlled trial “iMove” (n=17; 13 men and 4 women; age – 53.0 [49.0; 70.0] years; stroke duration – 10.0 [6.0; 13.0] months; upper limb paresis – 33.0 [12.0; 53.0] points on the FM-UL). Patients participated in a comprehensive rehabilitation program supplemented by BCI-guided movement imagery training (average of 9 training sessions).Results. Median of average BCI control rates achieved by the patients was 46.4 [44.2; 60.4]% in the NIRS group and 40.0 [35.7; 45.1]% in the EEG group (p=0.004). For the NIRS-BCI group, the median of the maximum BCI control accuracy achieved was 66.2 [56.4; 73.7]%, for EEGBCI – 50.6 [43.0; 62.3]% (p=0.006). The proportion of patients who achieved a clinically significant improvement according ARAT and the proportion of patients who achieved a clinically significant improvement according FM-UL were comparable in both groups. The NIRS-BCI group showed greater improvement in motor function compared to the EEG-BCI group according to Action Research Arm Test (ARAT; an increase of 5.0 [4.0; 8.0] points compared to an increase of 1.0 [0.0; 3.0] points; p=0.008), but not according to FM-UL scale (an increase of 5.0 [1.0; 10.0] and 4.0 [2.0; 5.0] points, respectively; p=0.455).Conclusion. NIRS-BCI has an advantage in control accuracy and ease of use in clinical practice. Achieving higher control accuracy of BCI provides additional opportunities for the use of game feedback scenarios to increase patient motivation.

Introduction. The high prevalence of post-COVID syndrome (PCS), which frequently manifests with emotional disturbances, cognitive impairment, and asthenia, necessitates effective rehabilitation methods. One potential approach is electroencephalography (EEG)-based biofeedback (BFB) therapy, though its use in PCS management has been explored in only a few studies to date. The study aimed to evaluate the effects of EEG α-rhythm BFB training on emotional state and cognitive function recovery, and reduction of astheniа symptoms in PCS patients. Materials and methods. Patients diagnosed with U09. Post-COVID-19 condition were randomly assigned to two groups of 10 participants each. The main group underwent 12–15 sessions of EEG α-rhythm BFB training using the NeuroPlay-6C headset with the Neurocorrection of COVID-19 Psychoemotional Consequences protocol, while the control group received identical training without biofeedback. Assessments performed before and after the intervention included: emotional state evaluation (State-Trait Anxiety Inventory [STAI], Short Health Anxiety Inventory [SHAI], Beck Depression Inventory [BDI], Psychological Stress Measure [PSM-25]), cognitive function assessment (Addenbrooke’s Cognitive Examination III [ACE-III], Schulte tables, Stroop test, Tower of London test, N-back test, 10-word memory test), assessment of asthenia (Multidimensional Fatigue Inventory [MFI]), and sleep quality evaluation (Insomnia Severity Index [ISI]). Results. In both groups, the training resulted in a significant reduction of personal anxiety, psychological stress, depression, and asthenia. The main group additionally demonstrated decreased health-related anxiety and improved information retention parameters. Intergroup comparison revealed more pronounced dynamics in the main group: greater reduction of general fatigue manifestations, increased immediate word recall volume, and improved retention of verbal information in working memory. The proportion of patients transitioning to milder symptom severity levels on individual scales was comparable between both groups. Conclusion. EEG α-rhythm biofeedback training can be implemented at the outpatient rehabilitation stage for PCS patients.

Introduction. The relevance of studying balance impairment in patients with Parkinson’s disease (PD) lies in the need to prevent falls and injuries while enabling patients to maintain maximum independence and mobility. Promising advances in posture and gait screening using digital image processing require a thorough understanding of fundamental balance maintenance strategies. The study was aimed at investigating balance maintenance strategies during PD “on” and “off” periods using classical and integral stabilometric parameters. Materials and methods. The study included 27 PD patients with the median of 61 years. The mean total daily levodopa equivalent dose was 889.71 mg. All patients underwent clinical balance assessment using the Berg Balance Scale and stabilometric platform testing during “on” and “off” periods. Results. Berg Balance Scale scores revealed mild balance impairments in PD patients, with greater severity during the “off” period (p 0.05). Classical Romberg test parameters during the “on” period demonstrated deteriorated balance function and increased reliance on visual strategies for balance maintenance. Analysis of vector integral parameters during the “off” period showed a significant increase in angular velocity and coefficient of abrupt direction changes (p 0.05). Stabilometry data indicate balance impairments in both PD “on” and “off” states, accompanied by different compensatory strategies. Conclusion. Despite clinical assessments suggesting only mild balance impairments and low fall risk in PD patients, stabilometric parameters revealed more significant static balance disorders contributing to fall risk. Notably, the diagnostic value of classical stabilometric parameters decreases during the “off” period, while vector parameters characterizing balance maintenance strategies gain importance. We propose that these integral parameters can effectively assess balance quality and fundamental compensatory strategies in PD patients undergoing treatment. The findings are valuable for developing digitalized balance analysis technologies incorporating artificial intelligence.

This paper presents an open dataset of over 50 hours of near infrared spectroscopy (NIRS) recordings. Fifteen stroke patients completed a total of 237 motor imagery brain–computer interface (BCI) sessions. The BCI was controlled by imagined hand movements; visual feedback was presented based on the real– time data classification results. We provide the experimental records, patient demographic profiles, clinical scores (including ARAT and Fugl–Meyer), online BCI performance, and a simple analysis of hemodynamic response. We assume that this dataset can be useful for evaluating the effectiveness of various near– infrared spectroscopy signal processing and analysis techniques in patients with cerebrovascular accidents.

Understanding patterns of interhemispheric asymmetry is crucial for monitoring neuroplastic changes during post-stroke motor rehabilitation. However, conventional laterality indices often pose computational challenges when applied to functional near-infrared spectroscopy (fNIRS) data due to the bidirectional hemodynamic responses. In this study, we analyze fNIRS recordings from 15 post-stroke patients undergoing motor imagery brain–computer interface training across multiple sessions. We compare traditional laterality coefficients with a novel task response asymmetry coefficient (TRAC), which quantifies differential hemispheric involvement during motor imagery tasks. Both indices are calculated for oxygenated and deoxygenated hemoglobin responses using general linear model coefficients, and their day-to-day dynamics are assessed with linear regression. The proposed TRAC demonstrates greater sensitivity than conventional measures, revealing significantly higher oxygenated hemoglobin TRAC values (0.18 ± 0.19 vs. −0.05 ± 0.20, p < 0.05) and lower deoxygenated hemoglobin TRAC values (−0.15 ± 0.27 vs. 0.04 ± 0.23, p < 0.05) in lesioned compared to intact hemispheres. Among patients who exhibit substantial motor recovery, distinct daily TRAC dynamics were observed, with statistically significant temporal trends. Overall, the novel TRAC metric offers enhanced discrimination of interhemispheric asymmetry patterns and captures temporal neuroplastic changes not detected by conventional indices, providing a more sensitive biomarker for tracking rehabilitation progress in post-stroke brain–computer interface applications.