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

作者：Qiwei Dong, Yuxi Zhou, Xiaoyu Xiong, Pengyu Liu, Jianfu Li, Cheng Luo, Diankun Gong, Li Dong, Dezhong Yao · 发表于：Brain Research Bulletin · 年份：2025 · DOI：10.1016/j.brainresbull.2025.111546 · 被引用次数：4 · 研究领域：EEG and Brain-Computer Interfaces、Functional Brain Connectivity Studies、Emotion and Mood Recognition

BACKGROUND: Electroencephalography (EEG) overcomes the subjectivity inherent in questionnaire-based and observational assessments. However, most existing EEG-based evaluation methods still impose discrete categorical states onto continuously varying neural dynamics, thereby neglecting the continuity of states. With the rise of neuroscience alliances, challenges such as batch-effects across datasets and inconsistencies introduced by diverse EEG electrode montages have become increasingly prominent. Therefore, a robust assessment framework that accommodates large‑scale, multi‑site EEG data is expected. METHODS: A normative model-based assessment framework was developed for large-scale, multi-site EEG data, with attention assessments used as illustrative examples. Normative models are first constructed using EEG features from 1212 young individuals, and quantile ranks are computed. Next, feature selection is performed, and elastic net regression and support vector regression are used to model distributed attention (DA) and focused attention (FA). The results from normative model-based features are compared with original features to demonstrate the advantage of quantile rank features. Finally, the model's test-retest reliability and generalizability are assessed. RESULTS: The framework identifies statistical differences (q < 0.05) in attention performance between the top and bottom 20 % participants on attention scales. EEG features demonstrated specific patterns related to accur...