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<Articles JournalTitle="Frontiers in Biomedical Technologies">
  <Article>
    <Journal>
      <PublisherName>Tehran University of Medical Sciences</PublisherName>
      <JournalTitle>Frontiers in Biomedical Technologies</JournalTitle>
      <Issn>2345-5837</Issn>
      <Volume>13</Volume>
      <Issue>1</Issue>
      <PubDate PubStatus="epublish">
        <Year>2026</Year>
        <Month>01</Month>
        <Day>20</Day>
      </PubDate>
    </Journal>
    <title locale="en_US">A Straightforward Approach to fNIRS Channel Selection for Distinguishing Mental States from Resting States: Effective in Both Subject-Dependent and Subject-Independent Classification Models</title>
    <FirstPage>117</FirstPage>
    <LastPage>132</LastPage>
    <AuthorList>
      <Author>
        <FirstName>Ateke</FirstName>
        <LastName>Goshvarpour</LastName>
        <affiliation locale="en_US">Department of Biomedical Engineering, Imam Reza International University, Mashhad, Razavi Khorasan, Iran</affiliation>
      </Author>
    </AuthorList>
    <History>
      <PubDate PubStatus="received">
        <Year>2024</Year>
        <Month>01</Month>
        <Day>30</Day>
      </PubDate>
      <PubDate PubStatus="accepted">
        <Year>2025</Year>
        <Month>01</Month>
        <Day>20</Day>
      </PubDate>
    </History>
    <abstract locale="en_US">Purpose: Functional Near-Infrared Spectroscopy (fNIRS) is a relatively novel tool that measures local hemodynamic changes, including oxygenated hemoglobin [Oxy-Hb], deoxygenated hemoglobin [Deoxy-Hb], and total hemoglobin [Tot-Hb]. Its safety, portability, non-invasiveness, and cost-effectiveness make it a preferred technique for designing Brain-Computer Interfaces (BCIs). This study aims to develop an accurate fNIRS-based BCI module for classifying mental tasks and the resting state.
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Materials and Methods: Rather than relying on conventional statistical features, our approach utilizes nonlinear indices derived from a 2D Poincar&#xE9; plot. These measures are computationally efficient and capable of revealing the underlying dynamics of the system. Our primary innovation lies in the development of a novel feature and selection method. We assessed mental task recognition in both subject-dependent and subject-independent classification modes.
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Results: Our findings demonstrated a maximum accuracy of 93.75% for subject-specific style and 91.67% for subject-independent style.
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Conclusion: In summary, the simplicity and high performance of the proposed framework suggest promising future directions for designing online fNIRS-based BCI systems.</abstract>
    <web_url>https://fbt.tums.ac.ir/index.php/fbt/article/view/930</web_url>
    <pdf_url>https://fbt.tums.ac.ir/index.php/fbt/article/download/930/547</pdf_url>
  </Article>
</Articles>
