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dc.contributor.authorLi, Yao
dc.date.accessioned2016-04-05T20:00:54Z
dc.date.available2016-04-05T20:00:54Z
dc.date.issued2015
dc.identifier.isbn9781339104119
dc.identifier.other1733671076
dc.identifier.urihttp://hdl.handle.net/10477/51795
dc.description.abstractIn this research, we explored a potential industrial manufacturing application of Brain Computer Interface (BCI). Using Brain Computer Interface, we can build a classification system with human cognition based on P300 wave in subjects' Electroencephalogram (EEG). We discussed the detection of P300 and build a classifier with AdaBoost algorithm to classify P300 wave. Then a BCI-robot system is created to complete an industrial task that a robot picking bad bolts out of good ones from a moving conveyor based on the classification result of P300 waves. The accuracy of bolt detection is 57%.
dc.languageEnglish
dc.sourceDissertations & Theses @ SUNY Buffalo,ProQuest Dissertations & Theses Global
dc.subjectApplied sciences
dc.titleHuman cognition assisted control of industrial robots in manufacturing
dc.typeDissertation/Thesis


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