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This study investigates the application of surrogate data analysis to electroencephalogram (EEG) signals for the early detection of mild cognitive impairment (MCI) and dementia. We employed the Fourier shuffle (FS) surrogate method and the Wayland algorithm to analyze EEG data from young and elderly participants during rest and while performing cognitive function tests. Our results indicate significant differences in the nonlinearity and complexity of EEG signals between age groups and cognitive states, suggesting potential biomarkers for early detection of cognitive decline. The findings provide insights into the changes in brain activity associated with aging and cognitive impairment, which may contribute to improved diagnostic and rehabilitation strategies for MCI and dementia.

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Surrogate Analysis of EEG Signals for Early Detection of Mild Cognitive Impairment and Dementia

  • 会议论文

  • 发表日期：

    2026年

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  • Kuwon Sumi,
  • Kakeru Amano,
  • Masumi Takada,
  • Hiroki Takada

摘要

This study investigates the application of surrogate data analysis to electroencephalogram (EEG) signals for the early detection of mild cognitive impairment (MCI) and dementia. We employed the Fourier shuffle (FS) surrogate method and the Wayland algorithm to analyze EEG data from young and elderly participants during rest and while performing cognitive function tests. Our results indicate significant differences in the nonlinearity and complexity of EEG signals between age groups and cognitive states, suggesting potential biomarkers for early detection of cognitive decline. The findings provide insights into the changes in brain activity associated with aging and cognitive impairment, which may contribute to improved diagnostic and rehabilitation strategies for MCI and dementia.