Decoding Finger Flexion from Band-Specific ECoG Signals in Humans
Identifieur interne : 001D35 ( Main/Merge ); précédent : 001D34; suivant : 001D36Decoding Finger Flexion from Band-Specific ECoG Signals in Humans
Auteurs : Nanying Liang [France] ; Laurent Bougrain [France]Source :
- Frontiers in Neuroscience [ 1662-4548 ] ; 2012.
English descriptors
- mix :
Abstract
This article presents the method that won the brain-computer interface (BCI) competition IV addressed to the prediction of the finger flexion from electrocorticogram (ECoG) signals. ECoG-based BCIs have recently drawn the attention from the community. Indeed, ECoG can provide higher spatial resolution and better signal quality than classical EEG recordings. It is also more suitable for long-term use. These characteristics allow to decode precise brain activities and to realize efficient ECoG-based neuroprostheses. Signal processing is a very important task in BCIs research for translating brain signals into commands. Here, we present a linear regression method based on the amplitude modulation of band-specific ECoG including a short-term memory for individual finger flexion prediction. The effectiveness of the method was proven by achieving the highest value of correlation coefficient between the predicted and recorded finger flexion values on data set 4 during the BCI competition IV.
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DOI: 10.3389/fnins.2012.00091
PubMed: 22754496
PubMed Central: 3384842
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ECoG-based BCIs have recently drawn the attention from the community. Indeed, ECoG can provide higher spatial resolution and better signal quality than classical EEG recordings. It is also more suitable for long-term use. These characteristics allow to decode precise brain activities and to realize efficient ECoG-based neuroprostheses. Signal processing is a very important task in BCIs research for translating brain signals into commands. Here, we present a linear regression method based on the amplitude modulation of band-specific ECoG including a short-term memory for individual finger flexion prediction. The effectiveness of the method was proven by achieving the highest value of correlation coefficient between the predicted and recorded finger flexion values on data set 4 during the BCI competition IV.</p></div></front><back><div1 type="bibliography"><listBibl><biblStruct><analytic><author><name sortKey="Acharya, S" uniqKey="Acharya S">S. 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ECoG-based BCIs have recently drawn the attention from the community. Indeed, ECoG can provide higher spatial resolution and better signal quality than classical EEG recordings. It is also more suitable for long-term use. These characteristics allow to decode precise brain activities and to realize efficient ECoG-based neuroprostheses. Signal processing is a very important task in BCIs research for translating brain signals into commands. Here, we present a linear regression method based on the amplitude modulation of band-specific ECoG including a short-term memory for individual finger flexion prediction. The effectiveness of the method was proven by achieving the highest value of correlation coefficient between the predicted and recorded finger flexion values on data set 4 during the BCI competition IV.</div></front></TEI></HAL><PMC><TEI><teiHeader><fileDesc><titleStmt><title xml:lang="en">Decoding Finger Flexion from Band-Specific ECoG Signals in Humans</title><author><name sortKey="Liang, Nanying" sort="Liang, Nanying" uniqKey="Liang N" first="Nanying" last="Liang">Nanying Liang</name><affiliation wicri:level="1"><nlm:aff id="aff1"><institution>Inria, Villers-lès-Nancy</institution><country>F-54600, France</country></nlm:aff><country xml:lang="fr">France</country><wicri:regionArea></wicri:regionArea></affiliation></author><author><name sortKey="Bougrain, Laurent" sort="Bougrain, Laurent" uniqKey="Bougrain L" first="Laurent" last="Bougrain">Laurent Bougrain</name><affiliation wicri:level="1"><nlm:aff id="aff1"><institution>Inria, Villers-lès-Nancy</institution><country>F-54600, France</country></nlm:aff><country xml:lang="fr">France</country><wicri:regionArea></wicri:regionArea></affiliation><affiliation wicri:level="1"><nlm:aff id="aff2"><institution>Lorraine Research Laboratory in Computer Science and its Applications, UMR 7503, Université de Lorraine, Vandoeuvre-lès-Nancy</institution><country>F-54506, France</country></nlm:aff><country xml:lang="fr">France</country><wicri:regionArea></wicri:regionArea></affiliation></author></titleStmt><publicationStmt><idno type="wicri:source">PMC</idno><idno type="pmid">22754496</idno><idno type="pmc">3384842</idno><idno type="url">http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3384842</idno><idno type="RBID">PMC:3384842</idno><idno type="doi">10.3389/fnins.2012.00091</idno><date when="2012">2012</date><idno type="wicri:Area/Pmc/Corpus">000078</idno><idno type="wicri:explorRef" wicri:stream="Pmc" wicri:step="Corpus" wicri:corpus="PMC">000078</idno><idno type="wicri:Area/Pmc/Curation">000078</idno><idno type="wicri:explorRef" wicri:stream="Pmc" wicri:step="Curation">000078</idno><idno type="wicri:Area/Pmc/Checkpoint">000053</idno><idno type="wicri:explorRef" wicri:stream="Pmc" wicri:step="Checkpoint">000053</idno><idno type="wicri:Area/Ncbi/Merge">000137</idno><idno type="wicri:Area/Ncbi/Curation">000135</idno><idno type="wicri:Area/Ncbi/Checkpoint">000135</idno><idno type="wicri:doubleKey">1662-4548:2012:Liang N:decoding:finger:flexion</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en" level="a" type="main">Decoding Finger Flexion from Band-Specific ECoG Signals in Humans</title><author><name sortKey="Liang, Nanying" sort="Liang, Nanying" uniqKey="Liang N" first="Nanying" last="Liang">Nanying Liang</name><affiliation wicri:level="1"><nlm:aff id="aff1"><institution>Inria, Villers-lès-Nancy</institution><country>F-54600, France</country></nlm:aff><country xml:lang="fr">France</country><wicri:regionArea></wicri:regionArea></affiliation></author><author><name sortKey="Bougrain, Laurent" sort="Bougrain, Laurent" uniqKey="Bougrain L" first="Laurent" last="Bougrain">Laurent Bougrain</name><affiliation wicri:level="1"><nlm:aff id="aff1"><institution>Inria, Villers-lès-Nancy</institution><country>F-54600, France</country></nlm:aff><country xml:lang="fr">France</country><wicri:regionArea></wicri:regionArea></affiliation><affiliation wicri:level="1"><nlm:aff id="aff2"><institution>Lorraine Research Laboratory in Computer Science and its Applications, UMR 7503, Université de Lorraine, Vandoeuvre-lès-Nancy</institution><country>F-54506, France</country></nlm:aff><country xml:lang="fr">France</country><wicri:regionArea></wicri:regionArea></affiliation></author></analytic><series><title level="j">Frontiers in Neuroscience</title><idno type="ISSN">1662-4548</idno><idno type="eISSN">1662-453X</idno><imprint><date when="2012">2012</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en"><p>This article presents the method that won the brain-computer interface (BCI) competition IV addressed to the prediction of the finger flexion from electrocorticogram (ECoG) signals. ECoG-based BCIs have recently drawn the attention from the community. Indeed, ECoG can provide higher spatial resolution and better signal quality than classical EEG recordings. It is also more suitable for long-term use. These characteristics allow to decode precise brain activities and to realize efficient ECoG-based neuroprostheses. Signal processing is a very important task in BCIs research for translating brain signals into commands. Here, we present a linear regression method based on the amplitude modulation of band-specific ECoG including a short-term memory for individual finger flexion prediction. The effectiveness of the method was proven by achieving the highest value of correlation coefficient between the predicted and recorded finger flexion values on data set 4 during the BCI competition IV.</p></div></front><back><div1 type="bibliography"><listBibl><biblStruct><analytic><author><name sortKey="Acharya, S" uniqKey="Acharya S">S. 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McFarland</name></author><author><name sortKey="Pfurtscheller, G" uniqKey="Pfurtscheller G">G. Pfurtscheller</name></author><author><name sortKey="Vaughan, T M" uniqKey="Vaughan T">T. M. Vaughan</name></author></analytic></biblStruct></listBibl></div1></back></TEI></PMC></double></record>Pour manipuler ce document sous Unix (Dilib)
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