A Genetic-Based Feature Selection Approach in the Identification of Left/Right Hand Motor Imagery for a Brain-Computer Interface

Electroencephalography is a non-invasive measure of the brain electrical activity generated by millions of neurons. Feature extraction in electroencephalography analysis is a core issue that may lead to accurate brain mental state classification. This paper presents a new feature selection method th...
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Autor*in:	Charles Yaacoub [verfasserIn] Georges Mhanna [verfasserIn] Sandy Rihana [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2017

Schlagwörter:	brain-computer interface feature selection genetic algorithm hand motor imagery neural networks

Übergeordnetes Werk:	In: Brain Sciences - MDPI AG, 2012, 7(2017), 1, p 12
Übergeordnetes Werk:	volume:7 ; year:2017 ; number:1, p 12

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/brainsci7010012

Katalog-ID:	DOAJ006319440

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allfieldsSound	10.3390/brainsci7010012 doi (DE-627)DOAJ006319440 (DE-599)DOAJ58a60087687a4329b5553a2bcc7aaecc DE-627 ger DE-627 rakwb eng RC321-571 Charles Yaacoub verfasserin aut A Genetic-Based Feature Selection Approach in the Identification of Left/Right Hand Motor Imagery for a Brain-Computer Interface 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Electroencephalography is a non-invasive measure of the brain electrical activity generated by millions of neurons. Feature extraction in electroencephalography analysis is a core issue that may lead to accurate brain mental state classification. This paper presents a new feature selection method that improves left/right hand movement identification of a motor imagery brain-computer interface, based on genetic algorithms and artificial neural networks used as classifiers. Raw electroencephalography signals are first preprocessed using appropriate filtering. Feature extraction is carried out afterwards, based on spectral and temporal signal components, and thus a feature vector is constructed. As various features might be inaccurate and mislead the classifier, thus degrading the overall system performance, the proposed approach identifies a subset of features from a large feature space, such that the classifier error rate is reduced. Experimental results show that the proposed method is able to reduce the number of features to as low as 0.5% (i.e., the number of ignored features can reach 99.5%) while improving the accuracy, sensitivity, specificity, and precision of the classifier. brain-computer interface feature selection genetic algorithm hand motor imagery neural networks Neurosciences. Biological psychiatry. Neuropsychiatry Georges Mhanna verfasserin aut Sandy Rihana verfasserin aut In Brain Sciences MDPI AG, 2012 7(2017), 1, p 12 (DE-627)687718139 (DE-600)2651993-8 20763425 nnns volume:7 year:2017 number:1, p 12 https://doi.org/10.3390/brainsci7010012 kostenfrei https://doaj.org/article/58a60087687a4329b5553a2bcc7aaecc kostenfrei http://www.mdpi.com/2076-3425/7/1/12 kostenfrei https://doaj.org/toc/2076-3425 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 7 2017 1, p 12
language	English
source	In Brain Sciences 7(2017), 1, p 12 volume:7 year:2017 number:1, p 12
sourceStr	In Brain Sciences 7(2017), 1, p 12 volume:7 year:2017 number:1, p 12
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institution	findex.gbv.de
topic_facet	brain-computer interface feature selection genetic algorithm hand motor imagery neural networks Neurosciences. Biological psychiatry. Neuropsychiatry
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authorswithroles_txt_mv	Charles Yaacoub @@aut@@ Georges Mhanna @@aut@@ Sandy Rihana @@aut@@
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callnumber-first	R - Medicine
author	Charles Yaacoub
spellingShingle	Charles Yaacoub misc RC321-571 misc brain-computer interface misc feature selection misc genetic algorithm misc hand motor imagery misc neural networks misc Neurosciences. Biological psychiatry. Neuropsychiatry A Genetic-Based Feature Selection Approach in the Identification of Left/Right Hand Motor Imagery for a Brain-Computer Interface
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topic_title	RC321-571 A Genetic-Based Feature Selection Approach in the Identification of Left/Right Hand Motor Imagery for a Brain-Computer Interface brain-computer interface feature selection genetic algorithm hand motor imagery neural networks
topic	misc RC321-571 misc brain-computer interface misc feature selection misc genetic algorithm misc hand motor imagery misc neural networks misc Neurosciences. Biological psychiatry. Neuropsychiatry
topic_unstemmed	misc RC321-571 misc brain-computer interface misc feature selection misc genetic algorithm misc hand motor imagery misc neural networks misc Neurosciences. Biological psychiatry. Neuropsychiatry
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title	A Genetic-Based Feature Selection Approach in the Identification of Left/Right Hand Motor Imagery for a Brain-Computer Interface
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title_auth	A Genetic-Based Feature Selection Approach in the Identification of Left/Right Hand Motor Imagery for a Brain-Computer Interface
abstract	Electroencephalography is a non-invasive measure of the brain electrical activity generated by millions of neurons. Feature extraction in electroencephalography analysis is a core issue that may lead to accurate brain mental state classification. This paper presents a new feature selection method that improves left/right hand movement identification of a motor imagery brain-computer interface, based on genetic algorithms and artificial neural networks used as classifiers. Raw electroencephalography signals are first preprocessed using appropriate filtering. Feature extraction is carried out afterwards, based on spectral and temporal signal components, and thus a feature vector is constructed. As various features might be inaccurate and mislead the classifier, thus degrading the overall system performance, the proposed approach identifies a subset of features from a large feature space, such that the classifier error rate is reduced. Experimental results show that the proposed method is able to reduce the number of features to as low as 0.5% (i.e., the number of ignored features can reach 99.5%) while improving the accuracy, sensitivity, specificity, and precision of the classifier.
abstractGer	Electroencephalography is a non-invasive measure of the brain electrical activity generated by millions of neurons. Feature extraction in electroencephalography analysis is a core issue that may lead to accurate brain mental state classification. This paper presents a new feature selection method that improves left/right hand movement identification of a motor imagery brain-computer interface, based on genetic algorithms and artificial neural networks used as classifiers. Raw electroencephalography signals are first preprocessed using appropriate filtering. Feature extraction is carried out afterwards, based on spectral and temporal signal components, and thus a feature vector is constructed. As various features might be inaccurate and mislead the classifier, thus degrading the overall system performance, the proposed approach identifies a subset of features from a large feature space, such that the classifier error rate is reduced. Experimental results show that the proposed method is able to reduce the number of features to as low as 0.5% (i.e., the number of ignored features can reach 99.5%) while improving the accuracy, sensitivity, specificity, and precision of the classifier.
abstract_unstemmed	Electroencephalography is a non-invasive measure of the brain electrical activity generated by millions of neurons. Feature extraction in electroencephalography analysis is a core issue that may lead to accurate brain mental state classification. This paper presents a new feature selection method that improves left/right hand movement identification of a motor imagery brain-computer interface, based on genetic algorithms and artificial neural networks used as classifiers. Raw electroencephalography signals are first preprocessed using appropriate filtering. Feature extraction is carried out afterwards, based on spectral and temporal signal components, and thus a feature vector is constructed. As various features might be inaccurate and mislead the classifier, thus degrading the overall system performance, the proposed approach identifies a subset of features from a large feature space, such that the classifier error rate is reduced. Experimental results show that the proposed method is able to reduce the number of features to as low as 0.5% (i.e., the number of ignored features can reach 99.5%) while improving the accuracy, sensitivity, specificity, and precision of the classifier.
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title_short	A Genetic-Based Feature Selection Approach in the Identification of Left/Right Hand Motor Imagery for a Brain-Computer Interface
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A Genetic-Based Feature Selection Approach in the Identification of Left/Right Hand Motor Imagery for a Brain-Computer Interface

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