Brain Computer Interface Design Using Neural Network Classification of Autoregressive Models of Mental Task Electroencephalogram Signals.

Brain Computer Interface Design Using Neural Network Classification of Autoregressive Models of Mental Task Electroencephalogram Signals.

Autoregressive(AR) feature extraction and neural network(NN) classification techniques are conducted using Electroencephalogram(EEG) signals extracted during mental tasks for Brain Computer Interface (BCI) design. The output of the BCI design could be used with a translation scheme such as Morse Cod...

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Bibliographic Details
Main Author: Huan, Nai Jen
Format: Thesis
Published: 2004
Subjects:
QA76.75-76.765 Computer software
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id my-mmu-ep.131
record_format uketd_dc
spelling my-mmu-ep.1312010-02-17T08:39:39Z Brain Computer Interface Design Using Neural Network Classification of Autoregressive Models of Mental Task Electroencephalogram Signals. 2004 Huan, Nai Jen QA76.75-76.765 Computer software Autoregressive(AR) feature extraction and neural network(NN) classification techniques are conducted using Electroencephalogram(EEG) signals extracted during mental tasks for Brain Computer Interface (BCI) design. The output of the BCI design could be used with a translation scheme such as Morse Code; to move a cursor around a screen or to control the prosthesis only by using thoughts. This introduces an invaluable means for paralyzed individuals to communicate with their external surroundings. 2004 Thesis http://shdl.mmu.edu.my/131/ http://vlib.mmu.edu.my/diglib/login/dlusr/login.php masters Multimedia University Research Library
institution Multimedia University
collection MMU Institutional Repository
topic QA76.75-76.765 Computer software
spellingShingle QA76.75-76.765 Computer software
Huan, Nai Jen
Brain Computer Interface Design Using Neural Network Classification of Autoregressive Models of Mental Task Electroencephalogram Signals.
description Autoregressive(AR) feature extraction and neural network(NN) classification techniques are conducted using Electroencephalogram(EEG) signals extracted during mental tasks for Brain Computer Interface (BCI) design. The output of the BCI design could be used with a translation scheme such as Morse Code; to move a cursor around a screen or to control the prosthesis only by using thoughts. This introduces an invaluable means for paralyzed individuals to communicate with their external surroundings.
format Thesis
qualification_level Master's degree
author Huan, Nai Jen
author_facet Huan, Nai Jen
author_sort Huan, Nai Jen
title Brain Computer Interface Design Using Neural Network Classification of Autoregressive Models of Mental Task Electroencephalogram Signals.
title_short Brain Computer Interface Design Using Neural Network Classification of Autoregressive Models of Mental Task Electroencephalogram Signals.
title_full Brain Computer Interface Design Using Neural Network Classification of Autoregressive Models of Mental Task Electroencephalogram Signals.
title_fullStr Brain Computer Interface Design Using Neural Network Classification of Autoregressive Models of Mental Task Electroencephalogram Signals.
title_full_unstemmed Brain Computer Interface Design Using Neural Network Classification of Autoregressive Models of Mental Task Electroencephalogram Signals.
title_sort brain computer interface design using neural network classification of autoregressive models of mental task electroencephalogram signals.
granting_institution Multimedia University
granting_department Research Library
publishDate 2004
_version_ 1747829089757036544

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