Feature extraction from the EEG for a Brain-Computer Interface using Genetic Matching pursuit Algorithm with Gabor Dictionary

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The electroencephalogram (EEG) isconsidered to be one of the most scientifically exploitedsignals recorded from a human's organism. The difficultyencountered in the processing of the EEG signal stemsfrom its highly non-stationary and complex nature. Itappears that time-frequency (t-f) analysis of theelectroencephalogram is one of the most popularapproaches to the extraction of knowledge about braindynamics. This paper presents the Matching Pursuit (MP)method used for obtaining t-f features from braincomputerinterface (BCl) data, in this case the EEGsignals recorded from a subject performing imaginationof left and right hand movement. The emphasis in thepaper is put on an effective realization of MP with Gabordictionary due to its intensive computational load.Genetic Algorithms (GAs) have been utilized to optimizethe performance of the t-f method under consideration,which has resulted in the implementation of a GeneticMatching Pursuit Algorithm (GMPA). The BCI data areclassified using linear discriminant analysis (LDA) basedon the set of features extracted with the help of theGMPA. The applicability of the technique to a BCI systemis verified on the basis of the classification accuracy (CA)rate.
LanguageEnglish
Title of host publicationUnknown Host Publication
Pages196-201
Number of pages6
Publication statusPublished - Sep 2004
EventIEEE SMC UK-RI chapter Conference - Derry
Duration: 1 Sep 2004 → …

Conference

ConferenceIEEE SMC UK-RI chapter Conference
Period1/09/04 → …

Fingerprint

Brain computer interface
Glossaries
Electroencephalography
Feature extraction
Discriminant analysis
Genetic algorithms
Processing

Cite this

@inproceedings{f043b4ace50640339c5e2d7c1c0df5ff,
title = "Feature extraction from the EEG for a Brain-Computer Interface using Genetic Matching pursuit Algorithm with Gabor Dictionary",
abstract = "The electroencephalogram (EEG) isconsidered to be one of the most scientifically exploitedsignals recorded from a human's organism. The difficultyencountered in the processing of the EEG signal stemsfrom its highly non-stationary and complex nature. Itappears that time-frequency (t-f) analysis of theelectroencephalogram is one of the most popularapproaches to the extraction of knowledge about braindynamics. This paper presents the Matching Pursuit (MP)method used for obtaining t-f features from braincomputerinterface (BCl) data, in this case the EEGsignals recorded from a subject performing imaginationof left and right hand movement. The emphasis in thepaper is put on an effective realization of MP with Gabordictionary due to its intensive computational load.Genetic Algorithms (GAs) have been utilized to optimizethe performance of the t-f method under consideration,which has resulted in the implementation of a GeneticMatching Pursuit Algorithm (GMPA). The BCI data areclassified using linear discriminant analysis (LDA) basedon the set of features extracted with the help of theGMPA. The applicability of the technique to a BCI systemis verified on the basis of the classification accuracy (CA)rate.",
author = "P Herman and G Prasad and TM McGinnity",
year = "2004",
month = "9",
language = "English",
pages = "196--201",
booktitle = "Unknown Host Publication",

}

Herman, P, Prasad, G & McGinnity, TM 2004, Feature extraction from the EEG for a Brain-Computer Interface using Genetic Matching pursuit Algorithm with Gabor Dictionary. in Unknown Host Publication. pp. 196-201, IEEE SMC UK-RI chapter Conference, 1/09/04.

Feature extraction from the EEG for a Brain-Computer Interface using Genetic Matching pursuit Algorithm with Gabor Dictionary. / Herman, P; Prasad, G; McGinnity, TM.

Unknown Host Publication. 2004. p. 196-201.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

TY - GEN

T1 - Feature extraction from the EEG for a Brain-Computer Interface using Genetic Matching pursuit Algorithm with Gabor Dictionary

AU - Herman, P

AU - Prasad, G

AU - McGinnity, TM

PY - 2004/9

Y1 - 2004/9

N2 - The electroencephalogram (EEG) isconsidered to be one of the most scientifically exploitedsignals recorded from a human's organism. The difficultyencountered in the processing of the EEG signal stemsfrom its highly non-stationary and complex nature. Itappears that time-frequency (t-f) analysis of theelectroencephalogram is one of the most popularapproaches to the extraction of knowledge about braindynamics. This paper presents the Matching Pursuit (MP)method used for obtaining t-f features from braincomputerinterface (BCl) data, in this case the EEGsignals recorded from a subject performing imaginationof left and right hand movement. The emphasis in thepaper is put on an effective realization of MP with Gabordictionary due to its intensive computational load.Genetic Algorithms (GAs) have been utilized to optimizethe performance of the t-f method under consideration,which has resulted in the implementation of a GeneticMatching Pursuit Algorithm (GMPA). The BCI data areclassified using linear discriminant analysis (LDA) basedon the set of features extracted with the help of theGMPA. The applicability of the technique to a BCI systemis verified on the basis of the classification accuracy (CA)rate.

AB - The electroencephalogram (EEG) isconsidered to be one of the most scientifically exploitedsignals recorded from a human's organism. The difficultyencountered in the processing of the EEG signal stemsfrom its highly non-stationary and complex nature. Itappears that time-frequency (t-f) analysis of theelectroencephalogram is one of the most popularapproaches to the extraction of knowledge about braindynamics. This paper presents the Matching Pursuit (MP)method used for obtaining t-f features from braincomputerinterface (BCl) data, in this case the EEGsignals recorded from a subject performing imaginationof left and right hand movement. The emphasis in thepaper is put on an effective realization of MP with Gabordictionary due to its intensive computational load.Genetic Algorithms (GAs) have been utilized to optimizethe performance of the t-f method under consideration,which has resulted in the implementation of a GeneticMatching Pursuit Algorithm (GMPA). The BCI data areclassified using linear discriminant analysis (LDA) basedon the set of features extracted with the help of theGMPA. The applicability of the technique to a BCI systemis verified on the basis of the classification accuracy (CA)rate.

M3 - Conference contribution

SP - 196

EP - 201

BT - Unknown Host Publication

ER -