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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Veröffentlicht in:	IEEE transactions on biomedical engineering Jg. 45; H. 3; S. 277 - 286
Hauptverfasser:	Anderson, C.W., Stolz, E.A., Shamsunder, S.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York, NY IEEE 01.03.1998 Institute of Electrical and Electronics Engineers
Schlagworte:	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
Online-Zugang:	Volltext
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