Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/63882
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dc.contributor.authorLi, Yue
dc.date.accessioned2015-05-19T09:23:46Z
dc.date.available2015-05-19T09:23:46Z
dc.date.copyright2015en_US
dc.date.issued2015
dc.identifier.urihttp://hdl.handle.net/10356/63882
dc.description.abstractThis project describes the process to design and implementation of real time application for Extreme Learning Machine on Brain-Computer interface area. The ELM was proposed by Dr. Huang GB in NTU, which is Single-hidden layer feed forward network can be applied as a reliable and efficiency classifier in many different area. The project worked closely with the research team in NTU to conduct the FBCSP feature extraction for Motor Image which aim to differentiate different types of brain signal when subject “thinking” left or right. And also apply this extracted feature to ELM in order to classify it in real time. This project can match the feature in real time with accuracy around 90%. Furthermore, in order to demo the result, we built a remote control car which controlled by the classification result from ELM classifier.en_US
dc.format.extent53 p.en_US
dc.language.isoenen_US
dc.rightsNanyang Technological University
dc.subjectDRNTU::Engineering::Electrical and electronic engineeringen_US
dc.titleEEG based mind controlled caren_US
dc.typeFinal Year Project (FYP)en_US
dc.contributor.supervisorLin Zhiping
dc.contributor.supervisorHuang Guangbinen_US
dc.contributor.schoolSchool of Electrical and Electronic Engineeringen_US
dc.description.degreeBachelor of Engineeringen_US
dc.contributor.organizationBMWen_US
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Appears in Collections:EEE Student Reports (FYP/IA/PA/PI)
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