Multivariate autoregressive models for classification of spontaneous electroencephalographic signals during mental tasks

This article explores the use of scalar and multivariate autoregressive (AR) models to extract features from the human electroencephalogram (EEG) with which mental tasks can be discriminated. This is part of a larger project to investigate the feasibility of using EEG to allow paralyzed persons to c...

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Vydáno v:	IEEE transactions on biomedical engineering Ročník 45; číslo 3; s. 277 - 286
Hlavní autoři:	Anderson, C.W., Stolz, E.A., Shamsunder, S.
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	New York, NY IEEE 01.03.1998 Institute of Electrical and Electronics Engineers
Témata:	Backpropagation Backpropagation algorithms Biological and medical sciences Brain modeling Correlation methods Eigenvalues and eigenfunctions Electrodiagnosis. Electric activity recording Electroencephalography Feasibility Studies Feature extraction Feedforward neural networks Humans Investigative techniques, diagnostic techniques (general aspects) Karhunen-Loeve transforms Mathematical models Mathematical transformations Matrix algebra Medical sciences Mental Processes - physiology Models, Statistical Multivariate Analysis Nervous system Neural networks Neural Networks (Computer) Regression Analysis Vectors Wheelchairs Human Central nervous system Electroencephalography Autoregressive model Neural network Multivariate analysis Mental activity Electrodiagnosis Biomedical data processing Multichannel circuit Classification Signal analysis Pattern extraction Brain (vertebrata)
ISSN:	0018-9294
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Popis
Shrnutí:	This article explores the use of scalar and multivariate autoregressive (AR) models to extract features from the human electroencephalogram (EEG) with which mental tasks can be discriminated. This is part of a larger project to investigate the feasibility of using EEG to allow paralyzed persons to control a device such as a wheelchair. EEG signals from four subjects were recorded while they performed two mental tasks. Quarter-second windows of six-channel EEG were transformed into four different representations: scalar AR model coefficients, multivariate AR coefficients, eigenvalues of a correlation matrix, and the Karhunen-Loeve transform of the multivariate AR coefficients. Feature vectors defined by these representations were classified with a standard, feedforward neural network trained via the error backpropagation algorithm. The four representations produced similar results, with the multivariate AR coefficients performing slightly better and more consistently with an average classification accuracy of 91.4% on novel, untrained, EEG signals.
Bibliografie:	ObjectType-Article-2 SourceType-Scholarly Journals-1 ObjectType-Feature-1 content type line 23
ISSN:	0018-9294
DOI:	10.1109/10.661153