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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主要作者:	Huan, Nai Jen
格式:	Thesis
出版:	2004
主题:	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

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

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