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Classifying mental tasks based on features of higher-order statistics from EEG signals in brain-computer interface

Zhou, SM and Gan, JQ and Sepulveda, F (2008) 'Classifying mental tasks based on features of higher-order statistics from EEG signals in brain-computer interface.' Information Sciences, 178 (6). 1629 - 1640. ISSN 0020-0255

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Abstract

In order to characterize the non-Gaussian information contained within the EEG signals, a new feature extraction method based on bispectrum is proposed and applied to the classification of right and left motor imagery for developing EEG-based brain-computer interface systems. The experimental results on the Graz BCI data set have shown that based on the proposed features, a LDA classifier, SVM classifier and NN classifier outperform the winner of the BCI 2003 competition on the same data set in terms of either the mutual information, the competition criterion, or misclassification rate. © 2007 Elsevier Inc. All rights reserved.

Item Type: Article
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
T Technology > TK Electrical engineering. Electronics Nuclear engineering
Divisions: Faculty of Science and Health > Computer Science and Electronic Engineering, School of
Depositing User: Users 161 not found.
Date Deposited: 12 Dec 2012 20:54
Last Modified: 23 Jan 2019 00:15
URI: http://repository.essex.ac.uk/id/eprint/4134

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