A Comprehensive Review of Endogenous EEG-Based BCIs for Dynamic Device Control
Electroencephalogram (EEG)-based brain–computer interfaces (BCIs) provide a novel approach for controlling external devices. BCI technologies can be important enabling technologies for people with severe mobility impairment. Endogenous paradigms, which depend on user-generated commands and do not ne...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-08-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/22/15/5802 |
| _version_ | 1797440625422893056 |
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| author | Natasha Padfield Kenneth Camilleri Tracey Camilleri Simon Fabri Marvin Bugeja |
| author_facet | Natasha Padfield Kenneth Camilleri Tracey Camilleri Simon Fabri Marvin Bugeja |
| author_sort | Natasha Padfield |
| collection | DOAJ |
| description | Electroencephalogram (EEG)-based brain–computer interfaces (BCIs) provide a novel approach for controlling external devices. BCI technologies can be important enabling technologies for people with severe mobility impairment. Endogenous paradigms, which depend on user-generated commands and do not need external stimuli, can provide intuitive control of external devices. This paper discusses BCIs to control various physical devices such as exoskeletons, wheelchairs, mobile robots, and robotic arms. These technologies must be able to navigate complex environments or execute fine motor movements. Brain control of these devices presents an intricate research problem that merges signal processing and classification techniques with control theory. In particular, obtaining strong classification performance for endogenous BCIs is challenging, and EEG decoder output signals can be unstable. These issues present myriad research questions that are discussed in this review paper. This review covers papers published until the end of 2021 that presented BCI-controlled dynamic devices. It discusses the devices controlled, EEG paradigms, shared control, stabilization of the EEG signal, traditional machine learning and deep learning techniques, and user experience. The paper concludes with a discussion of open questions and avenues for future work. |
| first_indexed | 2024-03-09T12:11:59Z |
| format | Article |
| id | doaj.art-a1dca57222e446ebae36e28008925213 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T12:11:59Z |
| publishDate | 2022-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-a1dca57222e446ebae36e280089252132023-11-30T22:51:54ZengMDPI AGSensors1424-82202022-08-012215580210.3390/s22155802A Comprehensive Review of Endogenous EEG-Based BCIs for Dynamic Device ControlNatasha Padfield0Kenneth Camilleri1Tracey Camilleri2Simon Fabri3Marvin Bugeja4Centre for Biomedical Cybernetics, University of Malta, MSD 2080 Msida, MaltaCentre for Biomedical Cybernetics, University of Malta, MSD 2080 Msida, MaltaDepartment of Systems and Control Engineering, University of Malta, MSD 2080 Msida, MaltaDepartment of Systems and Control Engineering, University of Malta, MSD 2080 Msida, MaltaDepartment of Systems and Control Engineering, University of Malta, MSD 2080 Msida, MaltaElectroencephalogram (EEG)-based brain–computer interfaces (BCIs) provide a novel approach for controlling external devices. BCI technologies can be important enabling technologies for people with severe mobility impairment. Endogenous paradigms, which depend on user-generated commands and do not need external stimuli, can provide intuitive control of external devices. This paper discusses BCIs to control various physical devices such as exoskeletons, wheelchairs, mobile robots, and robotic arms. These technologies must be able to navigate complex environments or execute fine motor movements. Brain control of these devices presents an intricate research problem that merges signal processing and classification techniques with control theory. In particular, obtaining strong classification performance for endogenous BCIs is challenging, and EEG decoder output signals can be unstable. These issues present myriad research questions that are discussed in this review paper. This review covers papers published until the end of 2021 that presented BCI-controlled dynamic devices. It discusses the devices controlled, EEG paradigms, shared control, stabilization of the EEG signal, traditional machine learning and deep learning techniques, and user experience. The paper concludes with a discussion of open questions and avenues for future work.https://www.mdpi.com/1424-8220/22/15/5802brain–computer interface (BCI)brain–machine interface (BMI)electroencephalogram (EEG)endogenouscontrolmotor imagery (MI) |
| spellingShingle | Natasha Padfield Kenneth Camilleri Tracey Camilleri Simon Fabri Marvin Bugeja A Comprehensive Review of Endogenous EEG-Based BCIs for Dynamic Device Control Sensors brain–computer interface (BCI) brain–machine interface (BMI) electroencephalogram (EEG) endogenous control motor imagery (MI) |
| title | A Comprehensive Review of Endogenous EEG-Based BCIs for Dynamic Device Control |
| title_full | A Comprehensive Review of Endogenous EEG-Based BCIs for Dynamic Device Control |
| title_fullStr | A Comprehensive Review of Endogenous EEG-Based BCIs for Dynamic Device Control |
| title_full_unstemmed | A Comprehensive Review of Endogenous EEG-Based BCIs for Dynamic Device Control |
| title_short | A Comprehensive Review of Endogenous EEG-Based BCIs for Dynamic Device Control |
| title_sort | comprehensive review of endogenous eeg based bcis for dynamic device control |
| topic | brain–computer interface (BCI) brain–machine interface (BMI) electroencephalogram (EEG) endogenous control motor imagery (MI) |
| url | https://www.mdpi.com/1424-8220/22/15/5802 |
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