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dc.contributor.authorNagabushan, Nareshen_US
dc.date.accessioned2019-06-15T08:01:12Z
dc.date.available2019-06-15T08:01:12Z
dc.date.issued2019-06-14
dc.identifier.othervt_gsexam:20927en_US
dc.identifier.urihttp://hdl.handle.net/10919/90183
dc.description.abstractBrain-Computer Interfaces (BCIs) that rely on motor imagery currently allow subjects to control quad-copters, robotic arms, and computer cursors. Recent advancements have been made possible because of breakthroughs in fields such as electrical engineering, computer science, and neuroscience. Currently, most real-time BCIs use hand-crafted feature extractors, feature selectors, and classification algorithms. In this work, we explore the different classification algorithms currently used in electroencephalographic (EEG) signal classification and assess their performance on intracranial EEG (iEEG) data. We first discuss the motor imagery task employed using iEEG signals and find features that clearly distinguish between different classes. Second, we compare the different state-of-the-art classifiers used in EEG BCIs in terms of their error rate, computational requirements, and feature interpret-ability. Next, we show the effectiveness of these classifiers in iEEG BCIs and last, show that our new classification algorithm that is designed to use spatial, spectral, and temporal information reaches performance comparable to other state-of-the-art classifiers while also allowing increased feature interpret-ability.en_US
dc.format.mediumETDen_US
dc.publisherVirginia Techen_US
dc.rightsThis item is protected by copyright and/or related rights. Some uses of this item may be deemed fair and permitted by law even without permission from the rights holder(s), or the rights holder(s) may have licensed the work for use under certain conditions. For other uses you need to obtain permission from the rights holder(s).en_US
dc.subjectBrain-Computer-Interfaceen_US
dc.subjectiEEGen_US
dc.subjectEEGen_US
dc.subjectClassificationen_US
dc.titleAnalyzing and Classifying Neural Dynamics from Intracranial Electroencephalography Signals in Brain-Computer Interface Applicationsen_US
dc.typeThesisen_US
dc.contributor.departmentElectrical and Computer Engineeringen_US
dc.description.degreeMaster of Scienceen_US
thesis.degree.nameMaster of Scienceen_US
thesis.degree.levelmastersen_US
thesis.degree.grantorVirginia Polytechnic Institute and State Universityen_US
thesis.degree.disciplineComputer Engineeringen_US
dc.contributor.committeechairTokekar, Pratapen_US
dc.contributor.committeechairVijayan, Sujithen_US
dc.contributor.committeememberAbbott, Amos L.en_US


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