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  <Article>
    <Journal>
      <PublisherName>journalofhealthstudies</PublisherName>
      <JournalTitle>Indian Journal of Health Studies</JournalTitle>
      <PISSN>I</PISSN>
      <EISSN>S</EISSN>
      <Volume-Issue>Volume 8 Issues 1</Volume-Issue>
      <PartNumber/>
      <IssueTopic>Multidisciplinary</IssueTopic>
      <IssueLanguage>English</IssueLanguage>
      <Season>January 2026</Season>
      <SpecialIssue>N</SpecialIssue>
      <SupplementaryIssue>N</SupplementaryIssue>
      <IssueOA>Y</IssueOA>
      <PubDate>
        <Year>2026</Year>
        <Month>01</Month>
        <Day>31</Day>
      </PubDate>
      <ArticleType>Health Studies</ArticleType>
      <ArticleTitle>Dynamic Neural State Transitions Underlying Visuospatial Planning and Execution in Rubik__ampersandsignrsquo;s Cube Solving: An EEG Microstate Study</ArticleTitle>
      <SubTitle/>
      <ArticleLanguage>English</ArticleLanguage>
      <ArticleOA>Y</ArticleOA>
      <FirstPage>77</FirstPage>
      <LastPage>90</LastPage>
      <AuthorList>
        <Author>
          <FirstName/>
          <LastName/>
          <AuthorLanguage>English</AuthorLanguage>
          <Affiliation/>
          <CorrespondingAuthor>N</CorrespondingAuthor>
          <ORCID/>
        </Author>
      </AuthorList>
      <DOI/>
      <Abstract>EEG microstates provide a high-resolution temporal window into large-scale brain dynamics, yet their behavior during complex, real-world problem-solving remains underexplored. In this exploratory single-subject case study, EEG was recorded using a 19-channel system while the participant solved a Rubik__ampersandsignrsquo;s Cube across ten trials. Data were pre-processed in EEGLAB, artifact-corrected using independent component analysis and CleanRawData, and analyzed with the MicrostateLab plugin. Four microstate class structure (A__ampersandsignndash;D) were identified. Microstates A and C accounted for the greatest proportion of time coverage, while Microstates B and D occurred less frequently during task performance. Analysis of microstate transitions revealed recurring, non-random patterns across trials. These findings are descriptive and correlational in nature and demonstrate the feasibility of applying EEG microstate analysis to a complex problem-solving task. The study provides preliminary observations to inform future research using larger samples and controlled experimental designs to examine task-related microstate dynamics.</Abstract>
      <AbstractLanguage>English</AbstractLanguage>
      <Keywords>EEG, microstates, Rubik’s cube, problem solving, executive function</Keywords>
      <URLs>
        <Abstract>https://journalofhealthstudies.in/ubijournal-v1copy/journals/abstract.php?article_id=16119&amp;title=Dynamic Neural State Transitions Underlying Visuospatial Planning and Execution in Rubik__ampersandsignrsquo;s Cube Solving: An EEG Microstate Study</Abstract>
      </URLs>
      <References>
        <ReferencesarticleTitle>References</ReferencesarticleTitle>
        <ReferencesfirstPage>16</ReferencesfirstPage>
        <ReferenceslastPage>19</ReferenceslastPage>
        <References/>
      </References>
    </Journal>
  </Article>
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