Computer Engineering and Applications ›› 2020, Vol. 56 ›› Issue (19): 168-175.DOI: 10.3778/j.issn.1002-8331.1907-0310

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Research on Removing Ocular Artifacts from EEG by Using Energy Entropy and Peak Window

ZHOU Yuan, YU Ming, HUANG Weijia, LI Xiaolong   

  1. 1.College of Electrical and Information Engineering, Jiangsu University of Science and Technology, Zhenjiang, Jiangsu 212003, China
    2.Medical College, Affiliated Hospital of Jiangsu University, Zhenjiang, Jiangsu 212000, China
  • Online:2020-10-01 Published:2020-09-29



  1. 1.江苏科技大学 电子信息学院,江苏 镇江 212003
    2.江苏大学附属医院 医学院,江苏 镇江 212000


In order to improve the problems existing in the traditional Independent Component Analysis(ICA) algorithm for automatic removal of Ocular Artifact(OA), such as slow speed of recognition of OA, acquisition of synchronous reference EOG signals, and loss of EEG signals, a recognition method without reference Electro-Oculogram(EOG) signalsis proposed, which can automatically removal OA. Firstly, using the FastICA into independent component, calculate the spectrum energy entropy of each independent components, with energy spectrum entropy as the criterion to identify the OA components. Then the EEG signals in the OA component are isolated by the peak window and spliced with other independent components. Further more, the clean EEG signals are restored with the inverse algorithm of FastICA. These experimental results show that this method can quickly, precisely and automatically remove the OA and retain other EEG components. The average time of spectral energy entropy for the recognition of OA is 0.01 s, and the accuracy rate is 98%, which is suitable for real-time EOG removal.

Key words: Electroencephalography(EEG), Ocular Artifact(OA), energy entropy, peak window



关键词: 脑电信号(EEG), 眼电伪迹, 能量熵, 峰值窗口