Brain–Computer Interface Using Functional Near-Infrared Spectroscopy for Post-Stroke Motor Rehabilitation: Case Series

• Published in: Annaly kliničeskoj i èksperimentalʹnoj nevrologii (Online) Vol. 17; no. 4; pp. 82 - 88
• Main Authors: Lyukmanov, Roman Kh, Isaev, Mikhail R., Mokienko, Olesya A., Bobrov, Pavel D., Ikonnikova, Ekaterina S., Cherkasova, Anastasiia N., Suponeva, Natalia A.
• Format: Journal Article
• Language: English
• Published: Research Center of Neurology 05.01.2024
• Subjects: brain–computer interface; motor imagery; near-infrared spectroscopy; neuro-bio-control; rehabilitation; stroke
• ISSN: 2075-5473; 2409-2533
• DOI: 10.54101/ACEN.2023.4.10

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.