Decoding finger flexion from band-specific ECoG signals in humans
Identifieur interne : 001B20 ( Hal/Corpus ); précédent : 001B19; suivant : 001B21Decoding finger flexion from band-specific ECoG signals in humans
Auteurs : Nanying Liang ; Laurent BougrainSource :
- Frontiers in Neuroscience [ 1662-4548 ] ; 2012-06-28.
English descriptors
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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
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Hal:hal-00762316Le document en format XML
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<front><div type="abstract" xml:lang="en">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.</div>
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<email>laurent.bougrain@loria.fr</email>
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<idno type="stamp" n="INRIA">INRIA - Institut National de Recherche en Informatique et en Automatique</idno>
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<idno type="stamp" n="LORIA">LORIA - Laboratoire Lorrain de Recherche en Informatique et ses Applications</idno>
<idno type="stamp" n="LORIA-CSAI" p="LORIA">Systèmes complexes et intelligence artificielle</idno>
<idno type="stamp" n="UNIV-LORRAINE">Université de Lorraine</idno>
<idno type="stamp" n="INRIA_TEST">INRIA - Institut National de Recherche en Informatique et en Automatique</idno>
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<sourceDesc><biblStruct><analytic><title xml:lang="en">Decoding finger flexion from band-specific ECoG signals in humans</title>
<author role="aut"><persName><forename type="first">Nanying</forename>
<surname>Liang</surname>
</persName>
<idno type="halAuthorId">793169</idno>
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<author role="aut"><persName><forename type="first">Laurent</forename>
<surname>Bougrain</surname>
</persName>
<email>laurent.bougrain@loria.fr</email>
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<idno type="idHal">laurent-bougrain</idno>
<idno type="halAuthorId">790665</idno>
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<monogr><idno type="halJournalId" status="VALID">102190</idno>
<idno type="issn">1662-4548</idno>
<idno type="eissn">1662-453X</idno>
<title level="j">Frontiers in Neuroscience</title>
<imprint><publisher>Frontiers</publisher>
<biblScope unit="volume">6</biblScope>
<biblScope unit="issue">91</biblScope>
<date type="datePub">2012-06-28</date>
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<idno type="doi">10.3389/fnins.2012.00091</idno>
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<profileDesc><langUsage><language ident="en">English</language>
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<textClass><keywords scheme="author"><term xml:lang="en">BCI competition IV</term>
<term xml:lang="en">finger flexion</term>
<term xml:lang="en">linear regression</term>
<term xml:lang="en">feature selection</term>
<term xml:lang="en">brain-machine interface</term>
<term xml:lang="en">electrocorticography</term>
<term xml:lang="en">neuroprosthetics</term>
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<classCode scheme="halDomain" n="info.info-lg">Computer Science [cs]/Machine Learning [cs.LG]</classCode>
<classCode scheme="halDomain" n="sdv.neu">Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC]</classCode>
<classCode scheme="halTypology" n="ART">Journal articles</classCode>
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<abstract xml:lang="en">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.</abstract>
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