Combining brain-computer interface and virtual reality for rehabilitation in neurological diseases: A narrative review
- PMID: 32561504
- DOI: 10.1016/j.rehab.2020.03.015
Combining brain-computer interface and virtual reality for rehabilitation in neurological diseases: A narrative review
Abstract
Background: The traditional rehabilitation for neurological diseases lacks the active participation of patients, its process is monotonous and tedious, and the effects need to be improved. Therefore, a new type of rehabilitation technology with more active participation combining brain-computer interface (BCI) with virtual reality (VR) has developed rapidly in recent years and has been used in rehabilitation in neurological diseases.
Objectives: This narrative review analyzed and characterized the development and application of the new training system (BCI-VR) in rehabilitation of neurological diseases from the perspective of the BCI paradigm, to provide a pathway for future research in this field.
Methods: The review involved a search of the Web of Science-Science Citation Index/Social Sciences Citation Index and the China National Knowledge Infrastructure databases; 39 papers were selected. Advantages and challenges of BCI-VR - based neurological rehabilitation were analyzed in detail.
Results: Most BCI-VR studies included could be classified by 3 major BCI paradigms: motor imagery, P300, and steady-state visual-evoked potential. Integrating VR scenes into BCI systems could effectively promote the recovery process from nervous system injuries as compared with traditional methods.
Conclusion: As compared with rehabilitation based on traditional BCI, rehabilitation based on BCI-VR can provide better feedback information for patients and promote the recovery of brain function. By solving the challenges and continual development, the BCI-VR system can be broadly applied to the clinical treatment of various neurological diseases.
Keywords: Brain-computer interface; Neurological diseases; Rehabilitation; Virtual reality.
Copyright © 2020 Elsevier Masson SAS. All rights reserved.
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