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<p>This study explores the analysis of EEG signals to understand brain microstates and their dynamics in schizophrenia. Key aspects include:</p>
<ol>
<li>
<p><strong>Transition Matrix and Graphs</strong>: Creation of transition matrices and directed graphs to quantify and visualize EEG-derived microstate transitions in individuals with schizophrenia and healthy controls.</p>
</li>
<li>
<p><strong>Motif Analysis</strong>: Identification of recurring microstate transition patterns (motifs) within the complex networks, offering insights into neural circuitry differences in schizophrenia.</p>
</li>
<li>
<p><strong>Comparative Analysis</strong>: Utilization of statistical methods to compare motif distributions between groups, highlighting motifs that significantly differ in schizophrenia.</p>
</li>
<li>
<p><strong>Implications</strong>: The study discusses how certain EEG microstate motifs could serve as potential biomarkers for schizophrenia, aiding in diagnostics and therapeutic strategies.</p>
</li>
</ol>
<p>This research integrates statistical techniques and graph theory.</p>
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