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dc.contributor.authorShi, Yang
dc.date.accessioned2018-05-30T04:30:14Z
dc.date.available2018-05-30T04:30:14Z
dc.date.issued2017-12
dc.identifier.othershi_yang_201712_ms
dc.identifier.urihttp://purl.galileo.usg.edu/uga_etd/shi_yang_201712_ms
dc.identifier.urihttp://hdl.handle.net/10724/37928
dc.description.abstractThis thesis focuses on employing low cost EEG signals to control robots for navigation tasks. A data driven signal processing and machine learning framework is proposed and applied. Power Spectral Density (PSD) and Spectral Analysis are used for feature extraction, and I examined the result of Principle Component Analysis (PCA), and chose non-linear classifiers for machine learning. The algorithm for classification is Quadratic Discriminant Analysis (QDA), and achieved around 88% to 91% accuracy for five-fold cross validation. When testing with a new dataset, the accuracy is around 82%, but will be low in contaminated datasets and at varying electrode locations. I also experimented the real-time system, and most instructions are correctly classified. This thesis provides a novel system for EEG data processing, especially for situations of low cost, low channel amount equipment.
dc.languageeng
dc.publisheruga
dc.rightspublic
dc.subjectEEG, BCI, Brain wave, Machine learning, Quadratic Discriminant Analysis, Principle Component Analysis, Spectral Analysis, Power Spectral Density
dc.titleBrain controlled robot navigation based on low cost EEG
dc.typeThesis
dc.description.degreeMS
dc.description.departmentComputer Science
dc.description.majorComputer Science
dc.description.advisorWenZhan Song
dc.description.committeeWenZhan Song
dc.description.committeeTianming Liu
dc.description.committeeHamid R. Arabnia


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