Date of Award

1-1-2020

Language

English

Document Type

Master's Thesis

Degree Name

Master of Science (MS)

College/School/Department

Department of Electrical and Computer Engineering

Content Description

1 online resource (vii, 33 pages) : color illustrations.

Dissertation/Thesis Chair

Tolga Soyata

Committee Members

Aveek Dutta, Gary Saulnier

Keywords

BCI, CCA, EEG, MSI, SSVEP, Brain-computer interfaces, Evoked potentials (Electrophysiology), Electroencephalography

Subject Categories

Applied Mathematics | Biomedical Engineering and Bioengineering | Computer Engineering

Abstract

Brain-computer interfaces (BCI) provide an alternative communication method that does not require standard physical mediums (speech, typing, etc.). These systems have been implemented to provide additional communication and control options for people with certain motor disabilities. Classification is an important part of BCI systems and consists of inferring user commands from brain activity. Supervised classification methods often achieve higher accuracy, but unsupervised classification methods are useful when training is not practical for the user. This thesis focuses on unsupervised classification algorithms used for a BCI speller application and presents extensions for two existing classifiers that improve classification accuracy and thus the potential speed of the system. First, an extension to canonical correlation analysis (CCA) is proposed that uses temporally local covariance. Second, a generalized version of extended multivariate synchronization index (EMSI) is proposed that models brain activity as a dynamical system by utilizing time delay embedding. Both methods are evaluated on a common EEG dataset and both provide improved system speed in the average case.

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