Brain-Computer Interface Technology in Stroke Rehabilitation
DOI:
https://doi.org/10.61173/4y3cdc08Keywords:
BCI, stroke rehabilitation, signal decoding, neurofeedback training, neuroplasticityAbstract
Stroke survivors and their families have had to cope with major obstacles, but Brain-Computer Interface (BCI) technology is being touted as an innovation that is a game changer. Traditional modes of rehabilitation offer only minimal help to the severely impaired; however, BCI technology provides a unique ray of hope by enabling direct communication between the brain and devices for therapy and interactive purposes. This means not only restoring movement skills or improving thinking and talking abilities but also transforming the way a person interacts with their surroundings after having a stroke. Despite this potentiality, there are several challenges that must be overcome before BCI technology can be effectively applied in clinics, like signal decoding complexity and universality & accessibility hunt. It seeks to delve into the possibilities of BCI in rehabilitation after stroke, focusing on motor recovery, cognitive and communication restoration as well as environmental interactions. These include demonstrations of innovative uses such as fNIRS-based BCIs for controlling prosthetic limbs, neurofeedback training for cognitive enhancements, and making better use of customizing BCIs to improve mobility and communication. However, it also addresses the obstacles faced in signal acquisition, interpretation, personalization, adaptation, and the training necessary for effective BCI utilization. In order to tackle these issues, forward-looking algorithms, machine learning hybrid BCI systems, and adaptive learning systems were discussed, which mark out how these obstacles will be overcome in this detailed review. The article underscores interdisciplinary collaboration in addition to user-centric designs, ethical considerations regarding access, and continued innovation, which all speak towards a future where BCI technology not only overcomes current limitations but makes rehabilitation different, giving new hope and skills among stroke survivors again.
References
[1] Khan RA, Naseer N, Qureshi NK, Noori FM, Nazeer H, Khan Real-Time Feedback Loop: The real-time feedback loop MU. fNIRS-based Neurorobotic Interface for gait rehabilitation. is a critical aspect of adaptive learning systems that con- J Neuroeng Rehabil. 2018; 15(1):7. sistently monitor the person’s brain signals and system
[2] Noble SC, Woods E, Ward T, Ringwood JV. Accelerating responses. This information alters the system’s algorithms P300-based neurofeedback training for attention enhancement so that its interface adapts to changing needs while mainusing iterative learning control: a randomized controlled trial. J taining utmost performance levels. Neural Eng. 2024; 21(2). Personalization: Adaptive learning systems personalize
[3] Pirasteh A, Shamseini Ghiyasvand M, Pouladian M. EEG- each user’s experience with the BCI by matching interface based brain-computer interface methods with the aim of designs and control strategies with individual capabilities rehabilitating advanced stage ALS patients. Disabil Rehabil as well as preferences. Examples of personalization may Assist Technol. 2024:1-11. include adjusting signal detection sensitivity, choosing an
[4] Sun, S., & Zhou, J. A review of adaptive feature extraction alternative layout for an interface, or selecting effective and classification methods for EEG-based brain-computer feedback modes for a particular user. interfaces. 2014 International Joint Conference on Neural Incremental Learning: As opposed to retraining, the mod- Networks (IJCNN), 2014,1746-1753. el poses a flat advantage for these types of systems. Using
[5] Jafarifarmand, A., & Badamchizadeh, M.A. EEG Artifacts incremental 2learning methods, they can be exposed to Handling in a Real Practical Brain-Computer Interface new sets of data. In situations where there is a gradual im- Controlled Vehicle. IEEE Transactions on Neural Systems and provement or decline regarding patient conditions’ incre- Rehabilitation Engineering, 2019,27, 1200-1208. mental training proves valuable in rehabilitation contexts.
[6] Othman, A. Utilizing Brain-Computer Interface to Enhance Error Correction & Optimisation: Real-time error recognithe Learning Capacities of Individuals with Impairments: A tion capability inherent in adaptive learning systems helps Brief Review. International Journal of Research Publication and them correct their mistakes online. By examining instanc- Reviews.2022, 3(11),1248-1252 es where predictions from the system do not correspond to
[7] McFarland DJ, Wolpaw JR. Brain-computer interface use is a users’ intentions, these algorithms keep on enhancing their skill that the user and system acquire together. PLoS Biol. 2018; models for better accuracy and reduce false positives or 16(7):e2006719. negatives.
[8] Maier M, Ballester BR, Verschure PFMJ. Principles of Dean&Francis Neurorehabilitation After Stroke Based on Motor Learning and [14]Saha, S., & Baumert, M. Intra- and Inter-subject Variability Brain Plasticity Mechanisms. Front Syst Neurosci. 2019; 13:74. in EEG-Based Sensorimotor Brain Computer Interface: A
[9] Ishihara, W., Moxon, K.A., Ehrman, S., Yarborough, M., Review. Frontiers in Computational Neuroscience, 2020, 13. Panontin, T.L., & Nathan-Roberts, D. Feedback Modalities in [15]Hassouni A, Hoogendoorn M, van Otterlo M, et al. Brain–Computer Interfaces: A Systematic Review. Proceedings Personalization of health interventions using cluster-based of the Human Factors and Ergonomics Society Annual Meeting, reinforcement learning[C]//PRIMA 2018: Principles and Practice 2020, 64, 1186 - 1190. of Multi-Agent Systems: 21st International Conference, Tokyo,
[10] Angulo-Sherman, I.N., & Gutiérrez, D. Effect of different Japan, October 29-November 2, 2018, Proceedings 21. Springer feedback modalities in the performance of brain-computer International Publishing, 2018: 467-475. interfaces. 2014 International Conference on Electronics, [16]Kwon J, Shin J, Im CH. Toward a compact hybrid brain- Communications and Computers (CONIELECOMP), 2014, 14- computer interface (BCI): Performance evaluation of multi-class 21. hybrid EEG-fNIRS BCIs with limited number of channels. PLoS
[11] Pawar D, Dhage S N. Feature extraction methods for One. 2020; 15(3):e0230491. electroencephalography based brain-computer interface: a [17]Amiri S, Fazel-Rezai R, Asadpour V. A review of hybrid review[J]. Entropy, 2020, 1: 4. brain-computer interface systems[J]. Advances in Human-
[12] Murad A, Pyun J Y. Deep recurrent neural networks for Computer Interaction, 2013, 2013: 1-1. human activity recognition[J]. Sensors, 2017, 17(11): 2556. [18]Nguyen CH, Karavas GK, Artemiadis P. Adaptive multi-
[13] Da Silva-Sauer, L., Torre-Luque, A.D., Silva, J.S., degree of freedom Brain Computer Interface using online & Fernández-Calvo, B. New Perspectives for Cognitive feedback: Towards novel methods and metrics of mutual Rehabilitation: Could Brain-Computer Interface Systems Benefit adaptation between humans and machines for BCI. PLoS One. People With Dementia? Psychology & Neuroscience, 2019, 12, 2019;14(3):e0212620. 25–37.
Downloads
Published
Issue
Section
License
Copyright (c) 2024 by the authors.
This work is licensed under a Creative Commons Attribution 4.0 International License.
