A multi-label classification method for detection of combined motor imageries
Identifieur interne : 000288 ( Main/Exploration ); précédent : 000287; suivant : 000289A multi-label classification method for detection of combined motor imageries
Auteurs : Cecilia Lindig-Le N [France] ; Laurent Bougrain [France]Source :
English descriptors
Abstract
Imaginary motor tasks cause brain oscillations that can be detected through the analysis of electroencephalographic (EEG) recordings. The imagination of hands movement allows inducing up to three different brain states by considering the activity that each hand produces separately and the one caused by the combination of both. This article presents a new method to extend the classic Common Spatial Pattern (CSP) algorithm to a multi-class approach which analyses both brain hemispheres separately to solve, together with a stepwise classification strategy, a multi-label Brain-Computer Interface (BCI) problem. The considered approach is based upon the assumption that the brain activity induced by the motor imagery (MI) of the combination of both hands corresponds to the superposition of the activity generated during simple hand MIs. In this way, based on the event-related desynchronization that is detected within each brain hemisphere, the multi-classification task can be reduced into two binary-classification problems, leading to a much simpler recognition scheme that overcomes the drawback of the classical CSP method of being suitable to discriminate only between two classes. After testing the proposed approach over the EEG signals of six healthy subjects performing a four-class multi-label task involving simple and combined hand MIs together with the rest condition, results show that this technique is plausible for BCI control. In terms of accuracy, it outperforms the classical one-vs-one approach by 20% and has the same performance as the one-vs-all method. Nevertheless, to solve a multi-label classification problem involving k classes, the proposed method requires only log2(k) classifiers, whereas the one-vs-one method uses k(k-1)/2 classifiers and the one-vs-all k classifiers, thereby the new approach simplifies the classification task and seems promising for solving multi-label problems involving numerous classes. Index Terms—Brain-computer interfaces, EEG, motor imagery , sensorimotor rhythms, CSP.
Url:
Affiliations:
Links toward previous steps (curation, corpus...)
- to stream Hal, to step Corpus: 000744
- to stream Hal, to step Curation: 000744
- to stream Hal, to step Checkpoint: 000264
- to stream Main, to step Merge: 000288
- to stream Main, to step Curation: 000288
Le document en format XML
<record><TEI><teiHeader><fileDesc><titleStmt><title xml:lang="en">A multi-label classification method for detection of combined motor imageries</title>
<author><name sortKey="Lindig Le N, Cecilia" sort="Lindig Le N, Cecilia" uniqKey="Lindig Le N C" first="Cecilia" last="Lindig-Le N">Cecilia Lindig-Le N</name>
<affiliation wicri:level="1"><hal:affiliation type="institution" xml:id="struct-413289" status="VALID"><idno type="IdRef">157040569</idno>
<idno type="IdUnivLorraine">[UL]100--</idno>
<orgName>Université de Lorraine</orgName>
<orgName type="acronym">UL</orgName>
<date type="start">2012-01-01</date>
<desc><address><addrLine>34 cours Léopold - CS 25233 - 54052 Nancy cedex</addrLine>
<country key="FR"></country>
</address>
<ref type="url">http://www.univ-lorraine.fr/</ref>
</desc>
</hal:affiliation>
<country>France</country>
</affiliation>
</author>
<author><name sortKey="Bougrain, Laurent" sort="Bougrain, Laurent" uniqKey="Bougrain L" first="Laurent" last="Bougrain">Laurent Bougrain</name>
<affiliation wicri:level="1"><hal:affiliation type="researchteam" xml:id="struct-213693" status="VALID"><idno type="RNSR">201321089W</idno>
<orgName>Analysis and modeling of neural systems by a system neuroscience approach</orgName>
<orgName type="acronym">NEUROSYS</orgName>
<desc><address><country key="FR"></country>
</address>
<ref type="url">http://www.inria.fr/equipes/neurosys</ref>
</desc>
<listRelation><relation active="#struct-129671" type="direct"></relation>
<relation active="#struct-300009" type="indirect"></relation>
<relation active="#struct-423090" type="direct"></relation>
<relation active="#struct-206040" type="indirect"></relation>
<relation active="#struct-413289" type="indirect"></relation>
<relation name="UMR7503" active="#struct-441569" type="indirect"></relation>
</listRelation>
<tutelles><tutelle active="#struct-129671" type="direct"><org type="laboratory" xml:id="struct-129671" status="VALID"><idno type="RNSR">198618246Y</idno>
<orgName>INRIA Nancy - Grand Est</orgName>
<desc><address><addrLine>615 rue du Jardin Botanique 54600 Villers-lès-Nancy</addrLine>
<country key="FR"></country>
</address>
<ref type="url">http://www.inria.fr/nancy</ref>
</desc>
<listRelation><relation active="#struct-300009" type="direct"></relation>
</listRelation>
</org>
</tutelle>
<tutelle active="#struct-300009" type="indirect"><org type="institution" xml:id="struct-300009" status="VALID"><orgName>Institut National de Recherche en Informatique et en Automatique</orgName>
<orgName type="acronym">Inria</orgName>
<desc><address><addrLine>Domaine de VoluceauRocquencourt - BP 10578153 Le Chesnay Cedex</addrLine>
<country key="FR"></country>
</address>
<ref type="url">http://www.inria.fr/en/</ref>
</desc>
</org>
</tutelle>
<tutelle active="#struct-423090" type="direct"><org type="department" xml:id="struct-423090" status="VALID"><orgName>Department of Complex Systems, Artificial Intelligence & Robotics</orgName>
<orgName type="acronym">LORIA - AIS</orgName>
<desc><address><country key="FR"></country>
</address>
<ref type="url">http://www.loria.fr/la-recherche-en/departements/complex-system-and-artificial-intelligence</ref>
</desc>
<listRelation><relation active="#struct-206040" type="direct"></relation>
<relation active="#struct-300009" type="indirect"></relation>
<relation active="#struct-413289" type="indirect"></relation>
<relation name="UMR7503" active="#struct-441569" type="indirect"></relation>
</listRelation>
</org>
</tutelle>
<tutelle active="#struct-206040" type="indirect"><org type="laboratory" xml:id="struct-206040" status="VALID"><idno type="IdRef">067077927</idno>
<idno type="RNSR">198912571S</idno>
<idno type="IdUnivLorraine">[UL]RSI--</idno>
<orgName>Laboratoire Lorrain de Recherche en Informatique et ses Applications</orgName>
<orgName type="acronym">LORIA</orgName>
<date type="start">2012-01-01</date>
<desc><address><addrLine>Campus Scientifique BP 239 54506 Vandoeuvre-lès-Nancy Cedex</addrLine>
<country key="FR"></country>
</address>
<ref type="url">http://www.loria.fr</ref>
</desc>
<listRelation><relation active="#struct-300009" type="direct"></relation>
<relation active="#struct-413289" type="direct"></relation>
<relation name="UMR7503" active="#struct-441569" type="direct"></relation>
</listRelation>
</org>
</tutelle>
<tutelle active="#struct-413289" type="indirect"><org type="institution" xml:id="struct-413289" status="VALID"><idno type="IdRef">157040569</idno>
<idno type="IdUnivLorraine">[UL]100--</idno>
<orgName>Université de Lorraine</orgName>
<orgName type="acronym">UL</orgName>
<date type="start">2012-01-01</date>
<desc><address><addrLine>34 cours Léopold - CS 25233 - 54052 Nancy cedex</addrLine>
<country key="FR"></country>
</address>
<ref type="url">http://www.univ-lorraine.fr/</ref>
</desc>
</org>
</tutelle>
<tutelle name="UMR7503" active="#struct-441569" type="indirect"><org type="institution" xml:id="struct-441569" status="VALID"><idno type="ISNI">0000000122597504</idno>
<idno type="IdRef">02636817X</idno>
<orgName>Centre National de la Recherche Scientifique</orgName>
<orgName type="acronym">CNRS</orgName>
<date type="start">1939-10-19</date>
<desc><address><country key="FR"></country>
</address>
<ref type="url">http://www.cnrs.fr/</ref>
</desc>
</org>
</tutelle>
</tutelles>
</hal:affiliation>
<country>France</country>
<placeName><settlement type="city">Nancy</settlement>
<settlement type="city">Metz</settlement>
<region type="region" nuts="2">Grand Est</region>
<region type="old region" nuts="2">Lorraine (région)</region>
</placeName>
<orgName type="university">Université de Lorraine</orgName>
</affiliation>
</author>
</titleStmt>
<publicationStmt><idno type="wicri:source">HAL</idno>
<idno type="RBID">Hal:hal-01180399</idno>
<idno type="halId">hal-01180399</idno>
<idno type="halUri">https://hal.inria.fr/hal-01180399</idno>
<idno type="url">https://hal.inria.fr/hal-01180399</idno>
<date when="2015-10-09">2015-10-09</date>
<idno type="wicri:Area/Hal/Corpus">000744</idno>
<idno type="wicri:Area/Hal/Curation">000744</idno>
<idno type="wicri:Area/Hal/Checkpoint">000264</idno>
<idno type="wicri:explorRef" wicri:stream="Hal" wicri:step="Checkpoint">000264</idno>
<idno type="wicri:Area/Main/Merge">000288</idno>
<idno type="wicri:Area/Main/Curation">000288</idno>
<idno type="wicri:Area/Main/Exploration">000288</idno>
</publicationStmt>
<sourceDesc><biblStruct><analytic><title xml:lang="en">A multi-label classification method for detection of combined motor imageries</title>
<author><name sortKey="Lindig Le N, Cecilia" sort="Lindig Le N, Cecilia" uniqKey="Lindig Le N C" first="Cecilia" last="Lindig-Le N">Cecilia Lindig-Le N</name>
<affiliation wicri:level="1"><hal:affiliation type="institution" xml:id="struct-413289" status="VALID"><idno type="IdRef">157040569</idno>
<idno type="IdUnivLorraine">[UL]100--</idno>
<orgName>Université de Lorraine</orgName>
<orgName type="acronym">UL</orgName>
<date type="start">2012-01-01</date>
<desc><address><addrLine>34 cours Léopold - CS 25233 - 54052 Nancy cedex</addrLine>
<country key="FR"></country>
</address>
<ref type="url">http://www.univ-lorraine.fr/</ref>
</desc>
</hal:affiliation>
<country>France</country>
</affiliation>
</author>
<author><name sortKey="Bougrain, Laurent" sort="Bougrain, Laurent" uniqKey="Bougrain L" first="Laurent" last="Bougrain">Laurent Bougrain</name>
<affiliation wicri:level="1"><hal:affiliation type="researchteam" xml:id="struct-213693" status="VALID"><idno type="RNSR">201321089W</idno>
<orgName>Analysis and modeling of neural systems by a system neuroscience approach</orgName>
<orgName type="acronym">NEUROSYS</orgName>
<desc><address><country key="FR"></country>
</address>
<ref type="url">http://www.inria.fr/equipes/neurosys</ref>
</desc>
<listRelation><relation active="#struct-129671" type="direct"></relation>
<relation active="#struct-300009" type="indirect"></relation>
<relation active="#struct-423090" type="direct"></relation>
<relation active="#struct-206040" type="indirect"></relation>
<relation active="#struct-413289" type="indirect"></relation>
<relation name="UMR7503" active="#struct-441569" type="indirect"></relation>
</listRelation>
<tutelles><tutelle active="#struct-129671" type="direct"><org type="laboratory" xml:id="struct-129671" status="VALID"><idno type="RNSR">198618246Y</idno>
<orgName>INRIA Nancy - Grand Est</orgName>
<desc><address><addrLine>615 rue du Jardin Botanique 54600 Villers-lès-Nancy</addrLine>
<country key="FR"></country>
</address>
<ref type="url">http://www.inria.fr/nancy</ref>
</desc>
<listRelation><relation active="#struct-300009" type="direct"></relation>
</listRelation>
</org>
</tutelle>
<tutelle active="#struct-300009" type="indirect"><org type="institution" xml:id="struct-300009" status="VALID"><orgName>Institut National de Recherche en Informatique et en Automatique</orgName>
<orgName type="acronym">Inria</orgName>
<desc><address><addrLine>Domaine de VoluceauRocquencourt - BP 10578153 Le Chesnay Cedex</addrLine>
<country key="FR"></country>
</address>
<ref type="url">http://www.inria.fr/en/</ref>
</desc>
</org>
</tutelle>
<tutelle active="#struct-423090" type="direct"><org type="department" xml:id="struct-423090" status="VALID"><orgName>Department of Complex Systems, Artificial Intelligence & Robotics</orgName>
<orgName type="acronym">LORIA - AIS</orgName>
<desc><address><country key="FR"></country>
</address>
<ref type="url">http://www.loria.fr/la-recherche-en/departements/complex-system-and-artificial-intelligence</ref>
</desc>
<listRelation><relation active="#struct-206040" type="direct"></relation>
<relation active="#struct-300009" type="indirect"></relation>
<relation active="#struct-413289" type="indirect"></relation>
<relation name="UMR7503" active="#struct-441569" type="indirect"></relation>
</listRelation>
</org>
</tutelle>
<tutelle active="#struct-206040" type="indirect"><org type="laboratory" xml:id="struct-206040" status="VALID"><idno type="IdRef">067077927</idno>
<idno type="RNSR">198912571S</idno>
<idno type="IdUnivLorraine">[UL]RSI--</idno>
<orgName>Laboratoire Lorrain de Recherche en Informatique et ses Applications</orgName>
<orgName type="acronym">LORIA</orgName>
<date type="start">2012-01-01</date>
<desc><address><addrLine>Campus Scientifique BP 239 54506 Vandoeuvre-lès-Nancy Cedex</addrLine>
<country key="FR"></country>
</address>
<ref type="url">http://www.loria.fr</ref>
</desc>
<listRelation><relation active="#struct-300009" type="direct"></relation>
<relation active="#struct-413289" type="direct"></relation>
<relation name="UMR7503" active="#struct-441569" type="direct"></relation>
</listRelation>
</org>
</tutelle>
<tutelle active="#struct-413289" type="indirect"><org type="institution" xml:id="struct-413289" status="VALID"><idno type="IdRef">157040569</idno>
<idno type="IdUnivLorraine">[UL]100--</idno>
<orgName>Université de Lorraine</orgName>
<orgName type="acronym">UL</orgName>
<date type="start">2012-01-01</date>
<desc><address><addrLine>34 cours Léopold - CS 25233 - 54052 Nancy cedex</addrLine>
<country key="FR"></country>
</address>
<ref type="url">http://www.univ-lorraine.fr/</ref>
</desc>
</org>
</tutelle>
<tutelle name="UMR7503" active="#struct-441569" type="indirect"><org type="institution" xml:id="struct-441569" status="VALID"><idno type="ISNI">0000000122597504</idno>
<idno type="IdRef">02636817X</idno>
<orgName>Centre National de la Recherche Scientifique</orgName>
<orgName type="acronym">CNRS</orgName>
<date type="start">1939-10-19</date>
<desc><address><country key="FR"></country>
</address>
<ref type="url">http://www.cnrs.fr/</ref>
</desc>
</org>
</tutelle>
</tutelles>
</hal:affiliation>
<country>France</country>
<placeName><settlement type="city">Nancy</settlement>
<settlement type="city">Metz</settlement>
<region type="region" nuts="2">Grand Est</region>
<region type="old region" nuts="2">Lorraine (région)</region>
</placeName>
<orgName type="university">Université de Lorraine</orgName>
</affiliation>
</author>
</analytic>
</biblStruct>
</sourceDesc>
</fileDesc>
<profileDesc><textClass><keywords scheme="mix" xml:lang="en"><term>Brain-computer interfaces</term>
<term>CSP.</term>
<term>EEG</term>
<term>motor imagery</term>
<term>sensorimotor rhythms</term>
</keywords>
</textClass>
</profileDesc>
</teiHeader>
<front><div type="abstract" xml:lang="en">Imaginary motor tasks cause brain oscillations that can be detected through the analysis of electroencephalographic (EEG) recordings. The imagination of hands movement allows inducing up to three different brain states by considering the activity that each hand produces separately and the one caused by the combination of both. This article presents a new method to extend the classic Common Spatial Pattern (CSP) algorithm to a multi-class approach which analyses both brain hemispheres separately to solve, together with a stepwise classification strategy, a multi-label Brain-Computer Interface (BCI) problem. The considered approach is based upon the assumption that the brain activity induced by the motor imagery (MI) of the combination of both hands corresponds to the superposition of the activity generated during simple hand MIs. In this way, based on the event-related desynchronization that is detected within each brain hemisphere, the multi-classification task can be reduced into two binary-classification problems, leading to a much simpler recognition scheme that overcomes the drawback of the classical CSP method of being suitable to discriminate only between two classes. After testing the proposed approach over the EEG signals of six healthy subjects performing a four-class multi-label task involving simple and combined hand MIs together with the rest condition, results show that this technique is plausible for BCI control. In terms of accuracy, it outperforms the classical one-vs-one approach by 20% and has the same performance as the one-vs-all method. Nevertheless, to solve a multi-label classification problem involving k classes, the proposed method requires only log2(k) classifiers, whereas the one-vs-one method uses k(k-1)/2 classifiers and the one-vs-all k classifiers, thereby the new approach simplifies the classification task and seems promising for solving multi-label problems involving numerous classes. Index Terms—Brain-computer interfaces, EEG, motor imagery , sensorimotor rhythms, CSP.</div>
</front>
</TEI>
<affiliations><list><country><li>France</li>
</country>
<region><li>Grand Est</li>
<li>Lorraine (région)</li>
</region>
<settlement><li>Metz</li>
<li>Nancy</li>
</settlement>
<orgName><li>Université de Lorraine</li>
</orgName>
</list>
<tree><country name="France"><noRegion><name sortKey="Lindig Le N, Cecilia" sort="Lindig Le N, Cecilia" uniqKey="Lindig Le N C" first="Cecilia" last="Lindig-Le N">Cecilia Lindig-Le N</name>
</noRegion>
<name sortKey="Bougrain, Laurent" sort="Bougrain, Laurent" uniqKey="Bougrain L" first="Laurent" last="Bougrain">Laurent Bougrain</name>
</country>
</tree>
</affiliations>
</record>
Pour manipuler ce document sous Unix (Dilib)
EXPLOR_STEP=$WICRI_ROOT/Wicri/Lorraine/explor/InforLorV4/Data/Main/Exploration
HfdSelect -h $EXPLOR_STEP/biblio.hfd -nk 000288 | SxmlIndent | more
Ou
HfdSelect -h $EXPLOR_AREA/Data/Main/Exploration/biblio.hfd -nk 000288 | SxmlIndent | more
Pour mettre un lien sur cette page dans le réseau Wicri
{{Explor lien |wiki= Wicri/Lorraine |area= InforLorV4 |flux= Main |étape= Exploration |type= RBID |clé= Hal:hal-01180399 |texte= A multi-label classification method for detection of combined motor imageries }}
This area was generated with Dilib version V0.6.33. |