DEVELOPING A BRAIN COMPUTER INTERFACE CONTROL SYSTEM FOR ROBOTIC MOVEMENT IN TWO DIMENSIONS

Search for this publication on Google Scholar
Major, T. (2014). DEVELOPING A BRAIN COMPUTER INTERFACE CONTROL SYSTEM FOR ROBOTIC MOVEMENT IN TWO DIMENSIONS. Unc Charlotte Electronic Theses And Dissertations.
Download PDF

Analytics

57 views ◎
13 downloads ⇓


Abstract

This paper concerns the theory behind developing a brain computer interface (BCI) and the applications of such a system. Signal acquisition methods such as Functional Magnetic Resonance Imaging (fMRI), Near-Infrared Spectroscopy (NIRS), Magnetoencephalography (MEG), Electrocorticography (ECoG), and Electroencephalography (EEG) are discussed. There is also a review of the different types of Event Related Potentials (ERP) and signal extraction methods for generating filters from the captured data to generate a model for a BCI. Finally, this paper covers a review of notable BCIs that are being utilized in a wide range of applications and gives a working example using BCILAB to generate results from a sample data set using the techniques discussed in the paper.

Details
Author

Major, Tyler

Title

DEVELOPING A BRAIN COMPUTER INTERFACE CONTROL SYSTEM FOR ROBOTIC MOVEMENT IN TWO DIMENSIONS

Physical Description

1 online resource (64 pages) : PDF

Date

2014

Degree Granting Institution

University of North Carolina at Charlotte

Abstract

This paper concerns the theory behind developing a brain computer interface (BCI) and the applications of such a system. Signal acquisition methods such as Functional Magnetic Resonance Imaging (fMRI), Near-Infrared Spectroscopy (NIRS), Magnetoencephalography (MEG), Electrocorticography (ECoG), and Electroencephalography (EEG) are discussed. There is also a review of the different types of Event Related Potentials (ERP) and signal extraction methods for generating filters from the captured data to generate a model for a BCI. Finally, this paper covers a review of notable BCIs that are being utilized in a wide range of applications and gives a working example using BCILAB to generate results from a sample data set using the techniques discussed in the paper.

Genre

masters theses

Subjects--Topics

Electrical engineering

Degree

M.S.

Keywords

BCI
Brain Computer Interface
EEG

Subject Area

Electrical Engineering

Advisor(s)

Conrad, James

Committee Members

Kuyath, Stephen
Kakad, Yogendra

Degree Note

Thesis (M.S.)--University of North Carolina at Charlotte, 2014.

Rights Statement

This Item is protected by copyright and/or related rights. You are free to use this Item in any way that is permitted by the copyright and related rights legislation that applies to your use. For other uses you need to obtain permission from the rights-holder(s). For additional information, see http://rightsstatements.org/page/InC/1.0/.

Rights Holder Information

Copyright is held by the author unless otherwise indicated.

Identifier

Major_uncc_0694N_10725

Permalink

http://hdl.handle.net/20.500.13093/etd:1473