Decoding Finger Flexion from Band-Specific ECoG Signals in Humans
Identifieur interne : 000078 ( Pmc/Curation ); précédent : 000077; suivant : 000079Decoding Finger Flexion from Band-Specific ECoG Signals in Humans
Auteurs : Nanying Liang [France] ; Laurent Bougrain [France]Source :
- Frontiers in Neuroscience [ 1662-4548 ] ; 2012.
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.
Url:
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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<front><journal-meta><journal-id journal-id-type="nlm-ta">Front Neurosci</journal-id><journal-id journal-id-type="publisher-id">Front. Neurosci.</journal-id><journal-title-group><journal-title>Frontiers in Neuroscience</journal-title></journal-title-group><issn pub-type="ppub">1662-4548</issn><issn pub-type="epub">1662-453X</issn><publisher><publisher-name>Frontiers Research Foundation</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type="pmid">22754496</article-id><article-id pub-id-type="pmc">3384842</article-id><article-id pub-id-type="doi">10.3389/fnins.2012.00091</article-id><article-categories><subj-group subj-group-type="heading"><subject>Neuroscience</subject><subj-group><subject>Original Research</subject></subj-group></subj-group></article-categories><title-group><article-title>Decoding Finger Flexion from Band-Specific ECoG Signals in Humans</article-title></title-group><contrib-group><contrib contrib-type="author"><name><surname>Liang</surname><given-names>Nanying</given-names></name><xref ref-type="aff" rid="aff1"><sup>1</sup></xref></contrib><contrib contrib-type="author"><name><surname>Bougrain</surname><given-names>Laurent</given-names></name><xref ref-type="aff" rid="aff1"><sup>1</sup></xref><xref ref-type="aff" rid="aff2"><sup>2</sup></xref><xref ref-type="author-notes" rid="fn001">*</xref></contrib></contrib-group><aff id="aff1"><sup>1</sup><institution>Inria, Villers-lès-Nancy</institution><country>F-54600, France</country></aff><aff id="aff2"><sup>2</sup><institution>Lorraine Research Laboratory in Computer Science and its Applications, UMR 7503, Université de Lorraine, Vandoeuvre-lès-Nancy</institution><country>F-54506, France</country></aff><author-notes><fn fn-type="edited-by"><p>Edited by: Michael Tangermann, Berlin Institute of Technology, Germany</p></fn><fn fn-type="edited-by"><p>Reviewed by: Klaus R. Mueller, Technical University, Germany; Gerwin Schalk, Wadsworth Center, USA</p></fn><corresp id="fn001">*Correspondence: Laurent Bougrain, Lorraine Research Laboratory in Computer Science and its Applications, bat. C, Campus Scientifique, BP 239, 54506 Vandoeuvre-lès-Nancy Cedex, France. e-mail: <email>bougrain@loria.fr</email></corresp><fn fn-type="other" id="fn002"><p>This article was submitted to Frontiers in Neuroprosthetics, a specialty of Frontiers in Neuroscience.</p></fn></author-notes><pub-date pub-type="epub"><day>28</day><month>6</month><year>2012</year></pub-date><pub-date pub-type="collection"><year>2012</year></pub-date><volume>6</volume><elocation-id>91</elocation-id><history><date date-type="received"><day>20</day><month>1</month><year>2012</year></date><date date-type="accepted"><day>05</day><month>6</month><year>2012</year></date></history><permissions><copyright-statement>Copyright © 2012 Liang and Bougrain.</copyright-statement><copyright-year>2012</copyright-year><license license-type="open-access" xlink:href="http://www.frontiersin.org/licenseagreement"><license-p>This is an open-access article distributed under the terms of the <uri xlink:type="simple" xlink:href="http://creativecommons.org/licenses/by-nc/3.0/">Creative Commons Attribution Non Commercial License</uri>, which permits non-commercial use, distribution, and reproduction in other forums, provided the original authors and source are credited.</license-p></license></permissions><abstract><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></abstract><kwd-group><kwd>brain-machine interface</kwd><kwd>electrocorticography</kwd><kwd>neuroprosthetics</kwd><kwd>feature selection</kwd><kwd>linear regression</kwd><kwd>finger flexion</kwd><kwd>BCI competition IV</kwd></kwd-group><counts><fig-count count="4"></fig-count><table-count count="1"></table-count><equation-count count="3"></equation-count><ref-count count="20"></ref-count><page-count count="6"></page-count><word-count count="3552"></word-count></counts></article-meta></front></pmc></record>Pour manipuler ce document sous Unix (Dilib)
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