关键词: 脑电信号, 眼电伪迹, 独立成分分析, 自动去除

Abstract: In traditional blind source separation algorithms, they usually need two EOG signals as the references to eliminate EOG artifacts. However, when collecting the EOG signals, they will always easily make the subjects uncomfortable, and require manual identification. In order to solve these problems, a FastICA-based method is presented, which can automatically remove ocular artifacts. Firstly, the correlation coefficient between each independent component extracted by FastICA and GFP（Global Field Power） value is calculated. Secondly, compared with these correlation coefficients, the independent component that has the largest absolute value is identified as the independent component of the ocular artifact. Finally, the independent component is set zero to reconstruct the clean EEG signals so that the automatic removal of EOG artifacts is achieved. The 30 cases of experiment EEG data show that this method can quickly and precisely eliminate the ocular artifacts which is completely automatic, preserve the other EEG components, and can be applied in real-time occasions.

Key words: Electroencephalography（EEG）, ocular artifact, Independent Component Analysis（ICA）, automatic removal