A Survey of EEG and Machine Learning-Based Methods for Neural Rehabilitation
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 techno...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2023-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10268416/ |
| _version_ | 1797655661618659328 |
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| author | Jaiteg Singh Farman Ali Rupali Gill Babar Shah Daehan Kwak |
| author_facet | Jaiteg Singh Farman Ali Rupali Gill Babar Shah Daehan Kwak |
| author_sort | Jaiteg Singh |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-11T17:17:46Z |
| format | Article |
| id | doaj.art-ada2bfdd832a469f9c19db72f6524e16 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-03-11T17:17:46Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-ada2bfdd832a469f9c19db72f6524e162023-10-19T23:01:22ZengIEEEIEEE Access2169-35362023-01-011111415511417110.1109/ACCESS.2023.332106710268416A Survey of EEG and Machine Learning-Based Methods for Neural RehabilitationJaiteg Singh0https://orcid.org/0000-0002-2370-9384Farman Ali1https://orcid.org/0000-0002-9420-1588Rupali Gill2Babar Shah3https://orcid.org/0000-0002-5090-4695Daehan Kwak4https://orcid.org/0000-0001-5614-0190Chitkara University Institute of Engineering and Technology, Chitkara University, Punjab, IndiaDepartment of Computer Science and Engineering, School of Convergence, Sungkyunkwan University, Seoul, South KoreaChitkara University Institute of Engineering and Technology, Chitkara University, Punjab, IndiaCollege of Technological Innovation, Zayed University, Dubai, United Arab EmiratesDepartment of Computer Science and Technology, Kean University, Union, NJ, USAOne 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.https://ieeexplore.ieee.org/document/10268416/Brain–computer interface (BCI)EEGelectrocorticographyelectroencephalogram |
| spellingShingle | Jaiteg Singh Farman Ali Rupali Gill Babar Shah Daehan Kwak A Survey of EEG and Machine Learning-Based Methods for Neural Rehabilitation IEEE Access Brain–computer interface (BCI) EEG electrocorticography electroencephalogram |
| title | A Survey of EEG and Machine Learning-Based Methods for Neural Rehabilitation |
| title_full | A Survey of EEG and Machine Learning-Based Methods for Neural Rehabilitation |
| title_fullStr | A Survey of EEG and Machine Learning-Based Methods for Neural Rehabilitation |
| title_full_unstemmed | A Survey of EEG and Machine Learning-Based Methods for Neural Rehabilitation |
| title_short | A Survey of EEG and Machine Learning-Based Methods for Neural Rehabilitation |
| title_sort | survey of eeg and machine learning based methods for neural rehabilitation |
| topic | Brain–computer interface (BCI) EEG electrocorticography electroencephalogram |
| url | https://ieeexplore.ieee.org/document/10268416/ |
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