Comparison of linear, nonlinear, and feature selection methods for EEG signal classification.

Full Abstract

The reliable operation of brain-computer interfaces (BCIs) based on spontaneous electroencephalogram (EEG) signals requires accurate classification of multichannel EEG. The design of EEG representations and classifiers for BCI are open research questions whose difficulty stems from the need to extract complex spatial and temporal patterns from noisy multidimensional time series obtained from EEG measurements. The high-dimensional and noisy nature of EEG may limit the advantage of nonlinear classification methods over linear ones. This paper reports the results of a linear (linear discriminant analysis) and two nonlinear classifiers (neural networks and support vector machines) applied to the classification of spontaneous EEG during five mental tasks, showing that nonlinear classifiers produce only slightly better classification results. An approach to feature selection based on genetic algorithms is also presented with preliminary results of application to EEG during finger movement.

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Author information

Author/s: Garrett, Deon (D); Peterson, David A (DA); Anderson, Charles W (CW); Thaut, Michael H (MH);

Affiliation: Department of Computer Science, Colorado State University, Fort Collins 80523, USA. deong(-atsign-)acm.org

Journal and publication information

Publication Type: Comparative Study; Evaluation Studies; Journal Article; Research Support, U.S. Gov't, Non-P.H.S.; Validation Studies

Journal: IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society (IEEE Trans Neural Syst Rehabil Eng), published in United States. (Language: eng)

Reference: 2003-Jun; vol 11 (issue 2) : pp 141-4

Dates: Created 2003/08/05; Completed 2003/09/12; Revised 2006/11/15;

PMID: 12899257, status: MEDLINE (last retrieval date: 11/6/2008)

Sourced from the National Library of Medicine. Abstract text and other information may be subject to copyright.

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MeSH headings (categories)

This article was linked to the MESH Headings shown below.

Algorithms
Brain - physiology
Computer Simulation
Discriminant Analysis
Electroencephalography - classification; methods
Evoked Potentials - physiology
Fingers - physiology
Humans
Linear Models

Linear Models
Models, Neurological
Movement - physiology
Neural Networks (Computer)
Nonlinear Dynamics
Pattern Recognition, Automated
Signal Processing, Computer-Assisted
Thinking - physiology
User-Computer Interface

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