Source of Publication
One approach to therapy and training for the restoration of damaged muscles and motor systems is rehabilitation. EEG-assisted Brain-Computer Interface (BCI) may assist in restoring or enhancing ‘lost motor abilities in the brain. Assisted by brain activity, BCI offers simple-to-use technology aids and robotic prosthetics. This systematic literature review aims to explore the latest developments in BCI and motor control for rehabilitation. Additionally, we have explored typical EEG apparatuses that are available for BCI-driven rehabilitative purposes. Furthermore, a comparison of significant studies in rehabilitation assessment using machine learning techniques has been summarized. The results of this study may influence policymakers’ decisions regarding the use of EEG equipment, particularly wireless devices, to implement BCI technology. Moreover, the literature review results offer suggestions for further study and new research areas. We plan to identify the additional characteristics of each EEG equipment and determine which one is most suited for each industry by measuring the user experience based on various devices in future research.
Institute of Electrical and Electronics Engineers (IEEE)
Electroencephalography, Surveys, Visualization, Robots, Machine learning, Electromyography, Brain
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Singh, Jaiteg; Ali, Farman; Gill, Rupali; Shah, Babar; and Kwak, Daehan, "A Survey of EEG and Machine Learning-Based Methods for Neural Rehabilitation" (2023). All Works. 6145.
Indexed in Scopus
Open Access Type
Gold: This publication is openly available in an open access journal/series