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<!DOCTYPE article PUBLIC "-//NLM//DTD JATS (Z39.96) Journal Publishing DTD v1.3 20210610//EN" "https://jats.nlm.nih.gov/publishing/1.3/JATS-journalpublishing1-3.dtd">
<article article-type="research-article" dtd-version="1.3" xml:lang="ru">
  <front xmlns:xlink="http://www.w3.org/1999/xlink">
    <journal-meta>
      <journal-title-group>
        <journal-title>Computing, Telecommunication and Control</journal-title>
        <trans-title-group xml:lang="ru">
          <trans-title>Информатика, телекоммуникации и управление</trans-title>
        </trans-title-group>
      </journal-title-group>
      <issn pub-type="epub">2687-0517</issn>
    </journal-meta>
    <article-meta xmlns:xlink="http://www.w3.org/1999/xlink">
      <article-id pub-id-type="publisher-id">16</article-id>
      <title-group>
        <article-title>Brain activity pattern recognition based on symbolic regression</article-title>
        <trans-title-group xml:lang="ru">
          <trans-title>Распознавание паттернов мозговой активности на основе метода символьной регрессии</trans-title>
        </trans-title-group>
      </title-group>
      <contrib-group>
        <contrib contrib-type="author">
          <name>
            <surname>Sonkin</surname>
            <given-names>Konstantin</given-names>
          </name>
          <email>sonkinkonst@mail.ru</email>
        </contrib>
      </contrib-group>
      <pub-date publication-format="electronic" date-type="pub" iso-8601-date="2013-04-10">
        <day>10</day>
        <month>04</month>
        <year>2013</year>
      </pub-date>
      <issue>2</issue>
      <issue-id pub-id-type="publisher-id">169</issue-id>
      <fpage>117</fpage>
      <lpage>122</lpage>
      <self-uri xmlns:xlink="http://www.w3.org/1999/xlink" content-type="pdf" xlink:href="https://infocom.spbstu.ru/userfiles/files/articles/2013/2/16.pdf"/>
      <abstract xml:lang="en">
        <p>The task of electroencephalogram analysis is examined for the purpose of generation of effective recognition and categorization means. Symbolic regression method based on genetic programming is realized with the key advantage of automatic generation of regression model structure. Research results, given in the paper, represent the accuracy of short duration signals regression models at the level of 86 % upon the average.</p>
      </abstract>
      <kwd-group xml:lang="en">
        <kwd>analysis of EEG signals</kwd>
        <kwd>symbolic regression</kwd>
        <kwd>brain-computer interface</kwd>
        <kwd>brain activity patterns</kwd>
        <kwd>imagined movements</kwd>
      </kwd-group>
    </article-meta>
  </front>
</article>
