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Multivariate autoregressive models for classification of spontaneous electroencephalographic signals during mental tasks
1998
IEEE Transactions on Biomedical Engineering
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. Quartersecond windows of six-channel EEG were transformed into four different
doi:10.1109/10.661153
pmid:9509744
fatcat:fgphqxq3zrebjiwbyj6v3elele