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dc.contributor.authorGaur, Himanshu
dc.description.abstractA BCI or Brain Computer Interface is defined as a method of communication that converts neural activities generated by brain of living being (without the use of peripheral muscles and nerves) into computer commands or other device commands. BCI systems are useful for people with severe disability who have no reliable control over their muscles in order to interact with their surrounding environment. The BCI system used in this paper has used P300 evoked potential and three classifiers namely Logistic Regression (LR), Neural Network (NN), and Support Vector Machine (SVM). The system is tested with four people with severe disability and two able-bodied people. Classification accuracies obtained from LR, NN, SVM classifiers is then compared with Bayesian Linear Discriminant Analysis (BLDA) classifier and with each other. The relevant factors required for obtaining good classification accuracy in P300 evoked potential based BCI systems is also being explored and discussed.en_US
dc.publisherNorth Dakota State Universityen_US
dc.rightsNDSU Policy 190.6.2
dc.titleClassification Algorithms Applied to a Brain Computer Interface System Based On P300en_US
dc.typeMaster's paperen_US
dc.date.accessioned2017-08-25T21:16:40Z
dc.date.available2017-08-25T21:16:40Z
dc.date.issued2017
dc.identifier.urihttps://hdl.handle.net/10365/26386
dc.subject.lcshBrain-computer interfaces.en_US
dc.subject.lcshMachine learning. .en_US
dc.subject.lcshElectroencephalographyen_US
dc.subject.lcshEvoked potentials (Electrophysiology)en_US
dc.subject.lcshComputer algorithms.en_US
dc.subject.lcshSelf-help devices for people with disabilities.en_US
dc.rights.urihttps://www.ndsu.edu/fileadmin/policy/190.pdf
ndsu.degreeMaster of Science (MS)en_US
ndsu.collegeEngineeringen_US
ndsu.departmentComputer Scienceen_US
ndsu.programComputer Scienceen_US
ndsu.advisorLudwig, Simone


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