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        <datestamp>2026-09-16T06:20:41Z</datestamp>
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          <dc:title>Data Sheet 1_EEG functional connectivity for Parkinson’s disease diagnosis and non-motor symptom prediction: a machine learning approach.docx</dc:title>
          <dc:creator>Fanzun Meng (24961684)</dc:creator>
          <dc:creator>Shuo Liu (108276)</dc:creator>
          <dc:creator>Xiaoyu Xiao (2366371)</dc:creator>
          <dc:creator>Chuanmin Ma (9546870)</dc:creator>
          <dc:creator>Fangchun Huang (24961687)</dc:creator>
          <dc:creator>Zhuqing Xu (11602510)</dc:creator>
          <dc:creator>Zixiang Guo (12098909)</dc:creator>
          <dc:creator>Wenjing Jiang (7436471)</dc:creator>
          <dc:subject>Neurocognitive Patterns and Neural Networks</dc:subject>
          <dc:subject>EEG</dc:subject>
          <dc:subject>functional connectivity</dc:subject>
          <dc:subject>machine learning</dc:subject>
          <dc:subject>non-motor symptom</dc:subject>
          <dc:subject>Parkinson’s disease</dc:subject>
          <dc:description>Introduction&lt;p&gt;Parkinson’s disease (PD) is associated with severe non-motor symptoms, including mood disorders and sleep disturbances, whose neural mechanisms are linked to dysfunction in large-scale brain networks. Functional connectivity measured by electroencephalography (EEG) can noninvasively map abnormalities at the network level.&lt;/p&gt;Methods&lt;p&gt;This study employed the debiased weighted phase lag index (dwPLI) to mitigate the effects of volumetric conductivity and investigated EEG functional connectivity in PD patients (n = 14) and healthy controls (n = 14) during resting and emotional states. Diagnostic performance was evaluated using L1-regularized logistic regression combined with a strict subject-level leave-one-subject-out cross-validation method.&lt;/p&gt;Results&lt;p&gt;The accuracy of the beta band model combining the four states reached 82.14% (p &lt; 0.05, permutation test), while the accuracy of the gamma band model under the eyes-open state reached 89.29% (p &lt; 0.01). Furthermore, theta band connectivity during the sadness state significantly predicted PSQI scores in PD patients.&lt;/p&gt;Discussion&lt;p&gt;These findings suggest that emotion-induced network dynamics may reflect sleep disturbances in PD, and establish EEG functional connectivity as a candidate neurophysiological biomarker for PD, with potential applications in both diagnostic classification and the assessment of non-motor symptoms, although validation in larger cohorts is still required.&lt;/p&gt;</dc:description>
          <dc:date>2026-09-16T06:20:41Z</dc:date>
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          <dc:identifier>10.3389/fnhum.2026.1921946.s001</dc:identifier>
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          <dc:rights>CC BY 4.0</dc:rights>
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