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<article article-type="research-article" dtd-version="1.3" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xml:lang="ru"><front><journal-meta><journal-id journal-id-type="publisher-id">intechngu</journal-id><journal-title-group><journal-title xml:lang="ru">Вестник НГУ. Серия: Информационные технологии</journal-title><trans-title-group xml:lang="en"><trans-title>Vestnik NSU. Series: Information Technologies</trans-title></trans-title-group></journal-title-group><issn pub-type="ppub">1818-7900</issn><issn pub-type="epub">2410-0420</issn><publisher><publisher-name>НГУ</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type="doi">10.25205/1818-7900-2019-17-1-61-71</article-id><article-id custom-type="elpub" pub-id-type="custom">intechngu-78</article-id><article-categories><subj-group subj-group-type="heading"><subject>Research Article</subject></subj-group><subj-group subj-group-type="section-heading" xml:lang="ru"><subject>Статьи</subject></subj-group></article-categories><title-group><article-title>Метод проверки гипотез на базе статистической обработки разнородных электроэнцефалографических данных</article-title><trans-title-group xml:lang="en"><trans-title>Method of Hypotheses Proving Based on Statistical Processing of Heterogeneous EEG Data</trans-title></trans-title-group></title-group><contrib-group><contrib contrib-type="author" corresp="yes"><name-alternatives><name name-style="eastern" xml:lang="ru"><surname>Меркулова</surname><given-names>Е. А.</given-names></name><name name-style="western" xml:lang="en"><surname>Merkulova</surname><given-names>E. A.</given-names></name></name-alternatives><email xlink:type="simple">merkaterine@gmail.com</email><xref ref-type="aff" rid="aff-1"/></contrib><contrib contrib-type="author" corresp="yes"><name-alternatives><name name-style="eastern" xml:lang="ru"><surname>Зюбин</surname><given-names>В. Е.</given-names></name><name name-style="western" xml:lang="en"><surname>Zyubin</surname><given-names>V. E.</given-names></name></name-alternatives><email xlink:type="simple">zyubin@iae.nsk.su</email><xref ref-type="aff" rid="aff-2"/></contrib><contrib contrib-type="author" corresp="yes"><name-alternatives><name name-style="eastern" xml:lang="ru"><surname>Князев</surname><given-names>Г. Г.</given-names></name><name name-style="western" xml:lang="en"><surname>Knyazev</surname><given-names>G. G.</given-names></name></name-alternatives><email xlink:type="simple">knyazev@physiol.ru</email><xref ref-type="aff" rid="aff-1"/></contrib></contrib-group><aff-alternatives id="aff-1"><aff xml:lang="ru"><institution>НИИ физиологии и фундаментальной медицины</institution><country>Россия</country></aff><aff xml:lang="en"><institution>State Scientific-Research Institute of Physiology &amp; Basic Medicine</institution><country>Russian Federation</country></aff></aff-alternatives><aff-alternatives id="aff-2"><aff xml:lang="ru"><institution>Институт автоматики и электрометрии СО РАН</institution><country>Россия</country></aff><aff xml:lang="en"><institution>Institute of Automation and Electrometry SB RAS</institution><country>Russian Federation</country></aff></aff-alternatives><pub-date pub-type="collection"><year>2019</year></pub-date><pub-date pub-type="epub"><day>30</day><month>10</month><year>2020</year></pub-date><volume>17</volume><issue>1</issue><fpage>61</fpage><lpage>71</lpage><permissions><copyright-statement>Copyright &amp;#x00A9; Меркулова Е.А., Зюбин В.Е., Князев Г.Г., 2020</copyright-statement><copyright-year>2020</copyright-year><copyright-holder xml:lang="ru">Меркулова Е.А., Зюбин В.Е., Князев Г.Г.</copyright-holder><copyright-holder xml:lang="en">Merkulova E.A., Zyubin V.E., Knyazev G.G.</copyright-holder><license xml:lang="ru" license-type="creative-commons-attribution" xlink:href="https://creativecommons.org/licenses/by/4.0/" xlink:type="simple"><license-p>Данная работа распространяется под лицензией Creative Commons Attribution 4.0.</license-p></license><license xml:lang="en" license-type="creative-commons-attribution" xlink:href="https://creativecommons.org/licenses/by/4.0/" xlink:type="simple"><license-p>This work is licensed under a Creative Commons Attribution 4.0 License.</license-p></license></permissions><self-uri xlink:href="https://intechngu.elpub.ru/jour/article/view/78">https://intechngu.elpub.ru/jour/article/view/78</self-uri><abstract><p>Предложен подход для автоматизации проверки гипотез на основе обработки разнородных ЭЭГ-данных. Подход базируется на наборе MATLAB-скриптов, использующих библиотеки EEGLAB, NeuroElf, Alphasim, spm8, Mediation toolbox, и программном пакете sLORETA. Разработанный инструментарий позволяет проводить унификацию ЭЭГ-записей, сделанных на различном оборудовании, восстановление утраченных данных, 3D-реконструкцию источников мозговой активности по исходным ЭЭГ-данным и медиационный анализ.</p></abstract><trans-abstract xml:lang="en"><p>This article describes the automatization of the hypothesis proving with heterogeneous EEG data obtained using different equipment (Neuroscan and Brain Products). EEG data is recorded from the head surface of the subjects by a helmet, which has 118-128 electrodes (channels). The subject undergoes various tests (trials) during the experiment The EEG data is divided into parts, in accordance with the trials. The connection between electrodes and head is unreliable, some channels may be lost because of the signal weakness. The approach includes unification of EEG-recordings, restoring of lost channels, 3D reconstruction of brain activity and conducting a mediation analysis. Raw data is overwritten in a unified order. Unmatched channels are excluded. Single corrupted channels are restored by spherical spline interpolation. 3D localization of brain activity is based on the inverse problem. The localization is conducted in the functional areas of the cerebral cortex, according to the Talayrak atlas using the MN method. Reconstruction is carried out separately for each trial, and for each of the five standard frequency ranges. The results are recorded in the NIFTI format, focused on the voxel representation. Then a multi-level mediation analysis is carried out. The coordinates of the discovered clusters are compared to the brain map and serve as a basis for interpreting and verifying neurophysiological hypotheses. The approach was implemented as a set of MATLAB scripts, libraries: EEGLAB, NeuroElf, Alphasim, spm8, Mediation toolbox, and the sLORETA software package. The created tools have been practically tested in the processing of neurophysiological experiments on social interaction. Created scripts can be used to test a wide class of neurophysiological hypotheses.</p></trans-abstract><kwd-group xml:lang="ru"><kwd>проверка гипотез</kwd><kwd>электроэнцефалограмма</kwd><kwd>обработка экспериментальных данных</kwd><kwd>медиационный анализ</kwd></kwd-group><kwd-group xml:lang="en"><kwd>hypothesis proving</kwd><kwd>electroencephalography</kwd><kwd>data processing</kwd><kwd>3D reconstruction</kwd><kwd>mediation analysis</kwd></kwd-group></article-meta></front><back><ref-list><title>References</title><ref id="cit1"><label>1</label><citation-alternatives><mixed-citation xml:lang="ru">Зуенко Е. А., Шулунова А. А. 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