生物医学工程研究2026，Vol.45Issue(1)：22-27,6.DOI:10.19529/j.cnki.1672-6278.2026.01.04
基于数据扩充和迁移学习的脑电信号质量评估方法
A data augmentation and transfer learning-based method for electroencephalogram signal quality assessment
张开 1陈亚萍 2郭志巍 1盛美萍 1范金迪 3王梦琦 3冯国训2
作者信息
- 1. 西北工业大学,西安 710072||西北工业大学宁波研究院,宁波 315100
- 2. 宁波市民康医院,宁波 315032
- 3. 西北工业大学,西安 710072
- 折叠
摘要
Abstract
Aiming at the problems of difficult data acquisition and high costs with manual annotation in the actual assessment of electroencephalogram(EEG)signal quality,we proposed an EEG signal quality assessment method based on data augmentation and transfer learning.Firstly,the autoregressive model was used to fit the real and pure EEG signals.Secondly,by adding different levels of simulated artifacts to the EEG signals,a multi-quality distribution of simulated EEG was formed to construct the source domain dataset.Finally,multi-dimensional features were extracted and the support vector machine(SVM)was trained on the source domain model and feature alignment was achieved through the association alignment transfer learning method.Experimental results showed that the accura-cy,macro average precision,macro-average recall and macro-average F1-score of the transfer learning-enhanced SVM achieved 84.00%,81.06%,85.76%and 82.85%,respectively,significantly outperforming the baseline approach without transfer learning.The research combines data augmentation with transfer learning for EEG quality evaluation,can provide a new low-cost cross-scenario method for EEG signal quality assessment.关键词
脑电信号/脑电信号质量评估/模拟脑电信号/数据扩充/迁移学习/特征分析Key words
Electroencephalogram signal/Electroencephalogram signal quality assessment/Simulated electroencephalogram sig-nal/Data augmentation/Transfer learning/Feature analysis分类
医药卫生引用本文复制引用
张开,陈亚萍,郭志巍,盛美萍,范金迪,王梦琦,冯国训..基于数据扩充和迁移学习的脑电信号质量评估方法[J].生物医学工程研究,2026,45(1):22-27,6.
