Neurofeedback and Brain-Computer Interface-Based Methods for Post-stroke Rehabilitation
- PMID: 40434551
- DOI: 10.1007/s10484-025-09715-z
Neurofeedback and Brain-Computer Interface-Based Methods for Post-stroke Rehabilitation
Abstract
Stroke has been identified as a major public health concern and one of the leading causes contributing to long-term neurological disability. People suffering from stroke often present with upper limb paralysis impacting their quality of life and ability to work. Motor impairments in the upper limb represent the most prevalent symptoms in stroke sufferers. There is a need to develop novel intervention strategies that can be used as stand-alone techniques or combined with current gold standard post-stroke rehabilitation procedures. There was reported evidence about the utility of rehabilitation protocols with motor imagery (MI) used either alone or in combination with physical therapy resulting in enhancement of post-stroke functional recovery of paralyzed limbs. Brain-Computer Interface (BCI) and EEG neurofeedback (NFB) training can be considered as novel technologies to be used in conjunction with MI and motor attempt (MA) to enable direct translation of EEG induced by imagery or attempted movement to arrange training that has potential to enhance functional motor recovery of upper limbs after stroke. There are reported several controlled trials and multiple cases series that have shown that stroke patients are able to learn modulation of their EEG sensorimotor rhythm in BCI mode to control external devices, including exoskeletons, prosthetics, and such interventions were shown promise in facilitation of recovery in stroke sufferers. A review of the literature suggests there has been significant progress in the development of new methods for post-stroke rehabilitation procedures. There are reviewed findings supportive of NFB and BCI methods as evidence-based treatment for post-stroke motor function recovery.
Keywords: Brain–computer interface; Motor imagery; Neurofeedback; Rehabilitation; Sensorimotor rhythm; Stroke.
© 2025. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.
Conflict of interest statement
Declarations. Conflict of interest: The authors declare no competing interests.
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References
-
- Alimardani, M., Nishio, S., & Ishiguro, H. (2013). Humanlike robot hands controlled by brain activity arouse illusion of ownership in operators. Scientific Reports,3, 2396. https://doi.org/10.1038/srep02396 - DOI - PubMed - PMC
-
- Allison, B. Z., Brunner, C., Altstätter, C., Wagner, I. C., Grissmann, S., & Neuper, C. (2012). A hybrid ERD/SSVEP BCI for continuous simultaneous two dimensional cursor control. Journal of Neuroscience Methods,209(2), 299–307. https://doi.org/10.1016/j.jneumeth.2012.06.022 - DOI - PubMed
-
- Allison, B. Z., & Neuper, C. (2010). Could anyone use a BCI? In D. S. Tan & A. Nijholt (Eds.), Brain-computer interfaces: Applying our minds to human-computer interaction (Human–computer interaction series) (pp. 34–54). Springer.
-
- Allison, B. Z., Wolpaw, E. W., & Wolpaw, J. R. (2007). Brain-computer interface systems: Progress and prospects. Expert Review of Medical Devices,4(4), 463–474. https://doi.org/10.1586/17434440.4.4.463 - DOI - PubMed
-
- Ang, K. K., Chua, K. S., Phua, K. S., Wang, C., Chin, Z. Y., Kuah, C. W., Low, W., & Guan, C. (2015a). A randomized controlled trial of EEG-based motor imagery brain-computer interface robotic rehabilitation for stroke. Clinical EEG and Neuroscience,46(4), 310–320. https://doi.org/10.1177/1550059414522229 - DOI - PubMed
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