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

Huan, Nai Jen (2004) Brain Computer Interface Design Using Neural Network Classification of Autoregressive Models of Mental Task Electroencephalogram Signals. Masters thesis, Multimedia University.

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Abstract

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.

Item Type: Thesis (Masters)
Subjects: Q Science > QA Mathematics > QA75.5-76.95 Electronic computers. Computer science > QA76.75-76.765 Computer software
Divisions: Faculty of Computing and Informatics (FCI)
Depositing User: Mrs Norizan Hassan
Date Deposited: 16 Dec 2009 08:27
Last Modified: 17 Feb 2010 08:39
URI: http://shdl.mmu.edu.my/id/eprint/131

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