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Biomarkers.

Ming Gu1,2, Boxin Sun1,2, Tim Martin3

  • 1Michigan Alzheimer's Disease Research Center, Ann Arbor, MI, USA.

Alzheimer'S & Dementia : the Journal of the Alzheimer'S Association
|December 24, 2025
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Summary
This summary is machine-generated.

This study introduces a novel power-sensitive channel rejection (PSCR) method for electroencephalography (EEG) analysis. PSCR improves the detection of neural activity differences between normal cognition and mild cognitive impairment groups.

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Area of Science:

  • Neuroscience
  • Biomedical Engineering

Background:

  • Traditional electroencephalography (EEG) preprocessing interpolates signals from contaminated channels, reducing spatial resolution and hindering localized brain activity analysis.
  • This limitation is particularly problematic in task-based EEG studies requiring regional specificity.

Purpose of the Study:

  • To introduce a novel Power-Sensitive Channel Rejection (PSCR) approach for EEG signal processing.
  • To enhance the analysis of localized brain activity and improve the differentiation between normal cognition (NC) and mild cognitive impairment (MCI) in task-based EEG.

Main Methods:

  • Utilized 64-channel task-based EEG data from 82 African American participants (ages 60-90) with subjective cognitive complaints.
  • Developed a PSCR method that statistically identifies and excludes high-power artifact channels based on current source density (CSD) thresholds for each task period.
  • Preserved regional specificity by excluding artifact-laden channels before calculating region-of-interest (ROI) signals.

Main Results:

  • Compared event-related CSD before and after PSCR application.
  • Observed that PSCR application made differences in localized neural activity between NC and MCI groups more distinct.
  • Noted that MCI participants showed higher medial-central neural activity during the NoGo period, potentially indicating compensatory mechanisms.

Conclusions:

  • The PSCR approach effectively minimizes interference between ROIs, thereby improving the distinction of neural activity differences between NC and MCI.
  • This method holds potential for broader applications in EEG-based brain research, particularly for studies sensitive to regional brain function.