Distributed Control Of An Aut Onomous Wheelchair Using Steady Evoked Potential State Visual Based Brain Interface
Having the capability to control a wheelchair using brain signals would be a major benefit to patients suffering from motor disabling diseases. However, one major challenge facing such systems is the number of inputs needed over time by the patient for control. The objective of this study is to deve...
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| Format: | Final Year Project / Dissertation / Thesis |
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2021
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| Online Access: | http://eprints.utar.edu.my/4609/ http://eprints.utar.edu.my/4609/1/Danny_Ng_Wee_Kiat.pdf |
| _version_ | 1848886196795604992 |
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| author | Ng, Danny Wee Kiat |
| author_facet | Ng, Danny Wee Kiat |
| author_sort | Ng, Danny Wee Kiat |
| building | UTAR Institutional Repository |
| collection | Online Access |
| description | Having the capability to control a wheelchair using brain signals would be a major benefit to patients suffering from motor disabling diseases. However, one major challenge facing such systems is the number of inputs needed over time by the patient for control. The objective of this study is to develop a “hybrid” system that requires less inputs from a subject to operate a wheelchair compared to the ones driven directly using BCI. A distributed control system using an autonomous wheelchair with inputs from a steady-state visual evoked potentialbased brain-computer interface was developed to achieve the objective. A dualmode system was implemented in this study to allow the autonomous wheelchair to work in both unknown and known environments. Such system is suitable for a person with physical and mobility impairments. The developed system required an average of 16.6 selections compared to a BCI wheelchair with direct control where an average of 32.8 selections was needed to complete a navigation task in this study. The lower number of required inputs reduces the number of mental tasks by the subjects. This is the first system that incorporates robotic and BCI to control a wheelchair, relegating the responsibility of navigation control from the subjects to the navigation software. |
| first_indexed | 2025-11-15T19:34:39Z |
| format | Final Year Project / Dissertation / Thesis |
| id | utar-4609 |
| institution | Universiti Tunku Abdul Rahman |
| institution_category | Local University |
| last_indexed | 2025-11-15T19:34:39Z |
| publishDate | 2021 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | utar-46092022-08-25T18:19:21Z Distributed Control Of An Aut Onomous Wheelchair Using Steady Evoked Potential State Visual Based Brain Interface Ng, Danny Wee Kiat T Technology (General) Having the capability to control a wheelchair using brain signals would be a major benefit to patients suffering from motor disabling diseases. However, one major challenge facing such systems is the number of inputs needed over time by the patient for control. The objective of this study is to develop a “hybrid” system that requires less inputs from a subject to operate a wheelchair compared to the ones driven directly using BCI. A distributed control system using an autonomous wheelchair with inputs from a steady-state visual evoked potentialbased brain-computer interface was developed to achieve the objective. A dualmode system was implemented in this study to allow the autonomous wheelchair to work in both unknown and known environments. Such system is suitable for a person with physical and mobility impairments. The developed system required an average of 16.6 selections compared to a BCI wheelchair with direct control where an average of 32.8 selections was needed to complete a navigation task in this study. The lower number of required inputs reduces the number of mental tasks by the subjects. This is the first system that incorporates robotic and BCI to control a wheelchair, relegating the responsibility of navigation control from the subjects to the navigation software. 2021 Final Year Project / Dissertation / Thesis NonPeerReviewed application/pdf http://eprints.utar.edu.my/4609/1/Danny_Ng_Wee_Kiat.pdf Ng, Danny Wee Kiat (2021) Distributed Control Of An Aut Onomous Wheelchair Using Steady Evoked Potential State Visual Based Brain Interface. PhD thesis, UTAR. http://eprints.utar.edu.my/4609/ |
| spellingShingle | T Technology (General) Ng, Danny Wee Kiat Distributed Control Of An Aut Onomous Wheelchair Using Steady Evoked Potential State Visual Based Brain Interface |
| title | Distributed Control Of An Aut Onomous Wheelchair Using Steady Evoked Potential State Visual Based Brain Interface |
| title_full | Distributed Control Of An Aut Onomous Wheelchair Using Steady Evoked Potential State Visual Based Brain Interface |
| title_fullStr | Distributed Control Of An Aut Onomous Wheelchair Using Steady Evoked Potential State Visual Based Brain Interface |
| title_full_unstemmed | Distributed Control Of An Aut Onomous Wheelchair Using Steady Evoked Potential State Visual Based Brain Interface |
| title_short | Distributed Control Of An Aut Onomous Wheelchair Using Steady Evoked Potential State Visual Based Brain Interface |
| title_sort | distributed control of an aut onomous wheelchair using steady evoked potential state visual based brain interface |
| topic | T Technology (General) |
| url | http://eprints.utar.edu.my/4609/ http://eprints.utar.edu.my/4609/1/Danny_Ng_Wee_Kiat.pdf |