Date of Award

2017

Publisher

North Dakota State University

Document Type

Master's Paper

Degree Awarded

Master of Science (MS)

Program

Computer Science

Department

Computer Science

College

Engineering

Faculty Advisor

Ludwig, Simone

Description

A 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.

Rights

NDSU Policy 190.6.2

Rights Link

https://www.ndsu.edu/fileadmin/policy/190.pdf

Handle Identifier

https://hdl.handle.net/10365/26386

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