基于数据扩充和迁移学习的脑电信号质量评估方法OA

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.