An Electric Wheelchair Manipulating System Using SSVEP-Based BCI System
Most people with motor disabilities use a joystick to control an electric wheelchair. However, those who suffer from multiple sclerosis or amyotrophic lateral sclerosis may require other methods to control an electric wheelchair. This study implements an electroencephalography (EEG)-based brain–comp...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-09-01
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| Series: | Biosensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2079-6374/12/10/772 |
| _version_ | 1797474888887304192 |
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| author | Wen Chen Shih-Kang Chen Yi-Hung Liu Yu-Jen Chen Chin-Sheng Chen |
| author_facet | Wen Chen Shih-Kang Chen Yi-Hung Liu Yu-Jen Chen Chin-Sheng Chen |
| author_sort | Wen Chen |
| collection | DOAJ |
| description | Most people with motor disabilities use a joystick to control an electric wheelchair. However, those who suffer from multiple sclerosis or amyotrophic lateral sclerosis may require other methods to control an electric wheelchair. This study implements an electroencephalography (EEG)-based brain–computer interface (BCI) system and a steady-state visual evoked potential (SSVEP) to manipulate an electric wheelchair. While operating the human–machine interface, three types of SSVEP scenarios involving a real-time virtual stimulus are displayed on a monitor or mixed reality (MR) goggles to produce the EEG signals. Canonical correlation analysis (CCA) is used to classify the EEG signals into the corresponding class of command and the information transfer rate (ITR) is used to determine the effect. The experimental results show that the proposed SSVEP stimulus generates the EEG signals because of the high classification accuracy of CCA. This is used to control an electric wheelchair along a specific path. Simultaneous localization and mapping (SLAM) is the mapping method that is available in the robotic operating software (ROS) platform that is used for the wheelchair system for this study. |
| first_indexed | 2024-03-09T20:36:26Z |
| format | Article |
| id | doaj.art-e702bd8c8bb14e2ea687a90ae2f772cf |
| institution | Directory Open Access Journal |
| issn | 2079-6374 |
| language | English |
| last_indexed | 2024-03-09T20:36:26Z |
| publishDate | 2022-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Biosensors |
| spelling | doaj.art-e702bd8c8bb14e2ea687a90ae2f772cf2023-11-23T23:10:24ZengMDPI AGBiosensors2079-63742022-09-01121077210.3390/bios12100772An Electric Wheelchair Manipulating System Using SSVEP-Based BCI SystemWen Chen0Shih-Kang Chen1Yi-Hung Liu2Yu-Jen Chen3Chin-Sheng Chen4Graduate Institute of Automation Technology, National Taipei University of Technology, Taipei 10608, TaiwanDepartment of Mechatronics Control, Industrial Technology Research Institute, Hsinchu 310401, TaiwanDepartment of Mechanical Engineering, National Taiwan University of Science and Technology, Taipei 106335, TaiwanDepartment of Radiation Oncology, MacKay Memorial Hospital, Taipei 10449, TaiwanGraduate Institute of Automation Technology, National Taipei University of Technology, Taipei 10608, TaiwanMost people with motor disabilities use a joystick to control an electric wheelchair. However, those who suffer from multiple sclerosis or amyotrophic lateral sclerosis may require other methods to control an electric wheelchair. This study implements an electroencephalography (EEG)-based brain–computer interface (BCI) system and a steady-state visual evoked potential (SSVEP) to manipulate an electric wheelchair. While operating the human–machine interface, three types of SSVEP scenarios involving a real-time virtual stimulus are displayed on a monitor or mixed reality (MR) goggles to produce the EEG signals. Canonical correlation analysis (CCA) is used to classify the EEG signals into the corresponding class of command and the information transfer rate (ITR) is used to determine the effect. The experimental results show that the proposed SSVEP stimulus generates the EEG signals because of the high classification accuracy of CCA. This is used to control an electric wheelchair along a specific path. Simultaneous localization and mapping (SLAM) is the mapping method that is available in the robotic operating software (ROS) platform that is used for the wheelchair system for this study.https://www.mdpi.com/2079-6374/12/10/772brain–computer interface (BCI)steady-state visual evoked potential (SSVEP)augmented reality (AR)canonical correlation analysis (CCA)electric wheelchairsimultaneous localization and mapping (SLAM) |
| spellingShingle | Wen Chen Shih-Kang Chen Yi-Hung Liu Yu-Jen Chen Chin-Sheng Chen An Electric Wheelchair Manipulating System Using SSVEP-Based BCI System Biosensors brain–computer interface (BCI) steady-state visual evoked potential (SSVEP) augmented reality (AR) canonical correlation analysis (CCA) electric wheelchair simultaneous localization and mapping (SLAM) |
| title | An Electric Wheelchair Manipulating System Using SSVEP-Based BCI System |
| title_full | An Electric Wheelchair Manipulating System Using SSVEP-Based BCI System |
| title_fullStr | An Electric Wheelchair Manipulating System Using SSVEP-Based BCI System |
| title_full_unstemmed | An Electric Wheelchair Manipulating System Using SSVEP-Based BCI System |
| title_short | An Electric Wheelchair Manipulating System Using SSVEP-Based BCI System |
| title_sort | electric wheelchair manipulating system using ssvep based bci system |
| topic | brain–computer interface (BCI) steady-state visual evoked potential (SSVEP) augmented reality (AR) canonical correlation analysis (CCA) electric wheelchair simultaneous localization and mapping (SLAM) |
| url | https://www.mdpi.com/2079-6374/12/10/772 |
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