Mechanical control of a wheelchair by means of EEG signals

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dc.contributor.advisorVan den Heever, David Jacobusen_ZA
dc.contributor.authorVan Wyk, Ian Nicolaasen_ZA
dc.contributor.otherStellenbosch University. Faculty of Engineering. Dept. of Mechanical and Mechatronic Engineering.en_ZA
dc.date.accessioned2018-02-28T19:00:11Z
dc.date.accessioned2018-04-09T07:08:59Z
dc.date.available2018-02-28T19:00:11Z
dc.date.available2018-04-09T07:08:59Z
dc.date.issued2018-03
dc.descriptionThesis (MEng)--Stellenbosch University, 2018.en_ZA
dc.description.abstractENGLISH ABSTRACT: The aim of this project was to incorporate a low cost electric wheelchair by modifying an existing self-propelled one with a neuro headset. The headset must control the wheelchair by using EEG data and motor imagery. Binary and discriminant analysis classifiers were implemented with accuracies ranging between 44% and 68.75%. It is concluded that dynamic classification has a very low accuracy compared to using pre acquired data. Also motor imagery is not very well suited when used with the Epoc+ neuro headset. The wheelchair was never constructed, however it was concluded that the listed components are sufficient to create a cheaper alternative for an electric wheelchair.en_ZA
dc.description.abstractAFRIKAANSE OPSOMMING: Die doel van die projek was om `n lae koste elektriese rolstoel te inkorporeer deur `n bestaande stoot-rolstoel te modifiseer. Hierdie rolstoel moet deur `n neuro-kopstuk beheer word deur middle van EEG seine en motor beelde. Binêre en diskriminante analise klassifikasies was gebruik met akkuraathede tussen 44% en 68.75%. Hieruit word afgelei dat dinamiese klassifikasie minder akkuraat is as wanneer vooraf bepaalde waardes gebruik word. Ook is motor beelde nie `n baie goeie keuse wanneer die Epoc+ neuro-kopstuk gebruik word nie. Die rolstoel was nooit gebou nie, maar daar is afgelei dat die componente gelys in die tesis voldoende is om `n goedkoop alternatief vir `n elektriese rolstoel te bou.af_ZA
dc.format.extent92 pages : illustrationsen_ZA
dc.identifier.urihttp://hdl.handle.net/10019.1/103765
dc.language.isoen_ZAen_ZA
dc.publisherStellenbosch : Stellenbosch Universityen_ZA
dc.rights.holderStellenbosch Universityen_ZA
dc.subjectEEGen_ZA
dc.subjectWheelchairsen_ZA
dc.subjectUCTDen_ZA
dc.subjectMechanical movementsen_ZA
dc.titleMechanical control of a wheelchair by means of EEG signalsen_ZA
dc.typeThesisen_ZA
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