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.

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. Gait disorders are a common and severe consequence of stroke, limiting patient autonomy and quality of life. A functional near-infrared spectroscopy (NIRS)-based brain–computer interface (BCI) represents a promising technology for restoring motor functions; however, its application in gait rehabilitation remains insufficiently studied. Study aim: To evaluate the clinical applicability and technical feasibility of an NIRS-based BCI technology combined with a pneumatic plantar support load simulator (pneumatic orthosis) as an adjunct to comprehensive motor rehabilitation in post-stroke patients. Materials and methods. Seventeen patients were enrolled in the pilot study, of whom 15 (median age 58.0 [47.0; 64.0] years, median time since stroke 6.0 [3.0; 9.0] months) completed a course of 7–13 (median 10 [9; 10]) training sessions with the NIRS-BCI–pneumatic orthosis technology in addition to a comprehensive motor rehabilitation program. Motor function was assessed using the Fugl–Meyer Assessment scale for the lower extremity, the 10-Meter Walk Test, and the Timed Up and Go test. Results. A total of 147 NIRS-BCI–pneumatic orthosis training sessions were conducted, with a total median exposure of 229 minutes per patient. The median BCI classifier recognition rate for patients’ mental states was 54.93% [53.10; 69.70]. The median of the maximum achieved recognition rates was 75.57% [67.54; 87.14]. Characteristic hemodynamic activation patterns were identified in motor and associative cortical areas. Following the course, statistically significant improvements were noted on the Fugl–Meyer Assessment scale (from 19.0 [16.0; 24.0] to 24.0 [20.0; 25.0] points; p = 0.001) and the Timed Up and Go test (from 17.61 [14.21; 22.34] to 16.06 [13.00; 17.41] s; p = 0.041), but not on the 10-Meter Walk Test (p 0.05). Most patients tolerated the procedures satisfactorily; two participants withdrew early. Conclusion. The clinical applicability and technical feasibility of the NIRS-BCI–pneumatic orthosis technology for post-stroke gait rehabilitation have been confirmed. Randomized controlled trials are required to assess its clinical efficacy.