Preview

Medical Doctor and Information Technologies

Advanced search

Hardware-software complex for rehabilitation of patients with cognitive and motor disorders

https://doi.org/10.25881/18110193_2024_4_38

Abstract

This study is aimed at developing a hardware-software system (HSS) for the rehabilitation of patients with mild (subclinical) and severe disorders of cognitive processes and motor functions of the upper extremities based on the application of multimodal biofeedback (BFB) including transcranial magnetic stimulation (TMS).
Materials and Methods: Electroencephalography (EEG) data with additional channels for electromyogram (EMG) recordings data of healthy volunteers were used in the work. The spatial filter, linear discriminant analysis, augmented covariance matrix method with classification in the space of tangents in the Riemann manifold, and support vector method were used to classify imaginary movements.
Results: Based on the neurophysiological study and literature analysis, an HSS was developed for rehabilitation of patients with mild (subclinical) and severe impairments of cognitive processes and motor functions. The developed real-time algorithms were shown to have an average accuracy of 86% for motor act classification, 75% for imagination with an animated visual stimulus, and 73% for imagination with a static visual stimulus.
Conclusions: An effective and versatile HSS based on modern BCI algorithms has been developed for rehabilitation of patients with cognitive and motor disorders

About the Authors

V. M. Antipov
FSBI "NMIC TPM" of the Ministry of Health of Russia; Immanuel Kant Baltic Federal Universit
Russian Federation


A. A. Badarin
FSBI "NMIC TPM" of the Ministry of Health of Russia; Immanuel Kant Baltic Federal University
Russian Federation

PhD



S. A. Kurkin
Immanuel Kant Baltic Federal University
Russian Federation

DSc, Assoc. Prof.



A. R. Kiselev
FSBI "NMIC TPM" of the Ministry of Health of Russia
Russian Federation

DSc



A. E. Hramov
Immanuel Kant Baltic Federal University
Russian Federation

DSc, Prof



References

1. Khorev V, Kurkin S, Badarin A, et al. Review on the use of brain computer interface rehabilitation methods for treating mental and neurological conditions. J Integr Neurosci. 2024; 23(7): 125. doi: 10.31083/j.jin2307125.

2. Wang Z, Cao C, Chen L, et al. Multimodal neural response and effect assessment during a BCI-based neurofeedback training after stroke. Frontiers in Neuroscience. 2022; 16: 884420. doi: 10.3389/fnins.2022.884420.

3. Kotov SV, Isakova EV, Slyun'kova EV. Usage of brain-computer interface+ exoskeleton technology as a part of complex multimodal stimulation in the rehabilitation of patients with stroke. Zhurnal Nevrologii i Psikhiatrii imeni SS Korsakova. 2019; 119(12-2): 37-42. (In Russ.) doi: 10.17116/jnevro201911912237.

4. Grigorev NA, Savosenkov AO, Lukoyanov MV, et al. A BCI-Based Vibrotactile Neurofeedback Training Improves Motor Cortical Excitability During Motor Imagery. IEEE Trans Neural Syst Rehabil Eng. 2021; 29: 1583-1592. doi: 10.1109/TNSRE.2021.3102304.

5. Go AS, Mozaffarian D, Roger VL. Heart disease and stroke statistics-2014 update: a report from the American Heart Association. Circulation. 2014; 129(3): 28-292. doi: 10.1161/ 01.cir.0000441139.02102.80.

6. Cifu DX, Stewart DG. Factors affecting functional outcome after stroke: a critical review of rehabilitation interventions. Arch Phys Med Rehabil. 1999; 80(5): 35-39. doi: 10.1016/S0003-9993(99)90101-6.

7. Hramov AE, Maksimenko VA, Pisarchik AN. Physical principles of brain-computer interfaces and their applications for rehabilitation, robotics and control of human brain states. Physics Reports. 2021; 918: 1-133. doi: 10.1016/j.physrep.2021.03.002.

8. Bamdad M, Homayoon Z, Mohammad AA. Application of BCI systems in neurorehabilitation: a scoping review. Disabil Rehabil Assist Technol. 2015; 10(5): 355-364. doi: 10.3109/17483107.2014.961569.

9. Zhuang M, Wu Q, Wan F, et al. State-of-the-art non-invasive brain–computer interface for neural rehabilitation: A review. Journal of Neurorestoratology. 2020; 8(1): 12-25. doi: 10.26599/JNR.2020.9040001.

10. Kho AY, Liu KPY, Chung RCK. Meta- analysis on the effect of mental imagery on motor recovery of the hemiplegic upper extremity function. Aust Occup Ther J. 2014; 61(2): 38-48. doi: 10.1111/1440-1630.12084.

11. Jochumsen M, Khan Niazi I, Samran Navid M, et al. Online multi-class brain-computer interface for detection and classification of lower limb movement intentions and kinetics for stroke rehabilitation. Brain-Computer Interfaces. 2015; 2(4): 202-210. doi: 10.1080/2326263X.2015.1114978.

12. Kardam VS, Taran S, Pandey A. Motor imagery tasks based electroencephalogram signals classification using data-driven features. Neuroscience Informatics. 2023; 3(2): 100128. doi: 10.1016/j.neuri.2023.100128.


Review

For citations:


Antipov V.M., Badarin A.A., Kurkin S.A., Kiselev A.R., Hramov A.E. Hardware-software complex for rehabilitation of patients with cognitive and motor disorders. Medical Doctor and Information Technologies. 2024;(4):38-47. (In Russ.) https://doi.org/10.25881/18110193_2024_4_38

Views: 31


Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.


ISSN 1811-0193 (Print)
ISSN 2413-5208 (Online)