Feature extraction and subset selection for classifying single-trial ECoG during motor imagery.

Full Abstract

The electrocorticogram (ECoG) recorded from subdural electrodes is a kind of BCI signal source that has the potential to achieve good classification results. The feature extraction and its subset selection are crucial for increasing classification accuracy rate. This paper proposes a new algorithm for classifying single-trial ECoG during motor imagery. The nonlinear regressive coefficients between signals on 10 leads are extracted in two frequency bands 0-3 Hz and 8-30 Hz as classification features. A genetic algorithm is used for the selection of the optimal feature subset and a support vector machine for their evaluation. The generalization error of 7% is achieved on data set I of BCI Competition III.

Author information

Author/s: Wei, Qingguo (Q); Gao, Xiaorong (X); Gao, Shangkai (S);

Affiliation: Department of Electronic Engineering, Nanchang University, Nanchang 330029, China.

Journal and publication information

Publication Type: Evaluation Studies; Journal Article; Research Support, Non-U.S. Gov't

Journal: Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference (Conf Proc IEEE Eng Med Biol Soc), published in United States. (Language: eng)

Reference: 2006-; vol 1 (issue ) : pp 1589-92

Dates: Created 2007/10/23; Completed 2008/03/17;

PMID: 17946051, status: MEDLINE (last retrieval date: 2/18/2009, IMS Date: 18 Feb 2009 00:00:00)

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

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