Lee Sangmin S, Lee Kiwon, Kang Guiyeom
Annu Int Conf IEEE Eng Med Biol Soc. 2020 Jul;2020:932-935. doi: 10.1109/EMBC44109.2020.9175785.
Artifact removal is important for EEG signal processing because artifacts adversely affect analysis results. To preserve normal EEG signal, several methods based on independent component analysis (ICA) have been studied and artifacts are removed by discarding independent components (ICs) classified as artifacts. In this study, a method using Bayesian deep learning and attention module is presented to improve performance of the classifier for ICs. Probability value is computed through the method to predict if a component is artifact and to treat ambiguous inputs. The attention module achieves increasing classification accuracy and shows the map of the region where the classifier concentrates on.
去除伪迹对于脑电图（EEG）信号处理很重要，因为伪迹会对分析结果产生不利影响。为了保留正常的EEG信号，人们研究了几种基于独立成分分析（ICA）的方法，通过丢弃被分类为伪迹的独立成分（IC）来去除伪迹。在本研究中，提出了一种使用贝叶斯深度学习和注意力模块的方法，以提高IC分类器的性能。通过该方法计算概率值，以预测一个成分是否为伪迹，并处理模糊输入。注意力模块提高了分类准确率，并显示了分类器关注区域的图谱。
