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Related Experiment Video

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A normative model-based assessment framework for large-scale, multi-site EEG data.

Qiwei Dong1, Yuxi Zhou2, Xiaoyu Xiong2

  • 1The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China; Sichuan Institute for Brain Science and Brain-Inspired Intelligence, Chengdu, China; Research Unit of NeuroInformation, Chinese Academy of Medical Sciences, Chengdu 2019RU035, China.

Brain Research Bulletin
|September 14, 2025
PubMed
Summary
This summary is machine-generated.

A new normative model framework harmonizes large-scale, multi-site electroencephalography (EEG) data for reliable attention assessment. This approach improves upon existing methods by considering continuous neural dynamics and reducing inconsistencies across datasets.

Keywords:
Attention assessmentEEGMulti-site dataNormative modelingRegression models

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Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Psychology

Background:

  • Electroencephalography (EEG) offers objective assessment, surpassing subjective methods.
  • Current EEG evaluations often oversimplify continuous neural dynamics into discrete states.
  • Large-scale, multi-site EEG studies face challenges like batch effects and montage inconsistencies.

Purpose of the Study:

  • To develop a robust framework for harmonizing large-scale, multi-site EEG data.
  • To improve the reliability and efficiency of attention assessment using EEG.
  • To address limitations of existing EEG evaluation methods by accommodating continuous neural dynamics.

Main Methods:

  • Constructed normative models using EEG features from 1212 young individuals.
  • Employed quantile ranks, elastic net regression, and support vector regression for attention modeling.
  • Assessed test-retest reliability and generalizability of the developed framework.

Main Results:

  • Identified significant statistical differences in attention performance between top and bottom participant groups (q < 0.05).
  • EEG features showed distinct patterns related to accuracy and reaction time in distributed and focused attention tasks.
  • The normative model demonstrated superior predictive performance, enhanced stability, and interpretability with high reliability (ICC > 0.9).

Conclusions:

  • A normative model-based framework was proposed to harmonize multi-site EEG data.
  • The framework enables efficient and reliable attention assessment.
  • This approach shows potential for broader applications in EEG-based research and clinical practice.