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.

Full text not available from this repository.
Official URL: http://myto.perpun.net.my/metoalogin/logina.php

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: Ms Rosnani Abd Wahab
Date Deposited: 30 Jun 2010 07:13
Last Modified: 30 Jun 2010 07:13
URI: http://shdl.mmu.edu.my/id/eprint/747

Actions (login required)

View Item View Item