The non-invasive brain-computer interface (BCI) has gradually become a hot spot of current research, and it has been applied in many fields such as mental disorder detection and physiological monitoring. However, the electroencephalography (EEG) signals required by the non-invasive BCI can be easily contaminated by electrooculographic (EOG) artifacts, which seriously affects the analysis of EEG signals. Therefore, this paper proposed an improved independent component analysis method combined with a frequency filter, which automatically recognizes artifact components based on the correlation coefficient and kurtosis dual threshold. In this method, the frequency difference between EOG and EEG was used to remove the EOG information in the artifact component through frequency filter, so as to retain more EEG information. The experimental results on the public datasets and our laboratory data showed that the method in this paper could effectively improve the effect of EOG artifact removal and improve the loss of EEG information, which is helpful for the promotion of non-invasive BCI.
非侵入式脑-机接口已经逐步成为当前研究的热点，在精神障碍检测、生理监测等多方面都有所应用。但是非侵入式脑-机接口所需的脑电信号容易受到眼电伪迹污染，会严重影响对脑电信号的解码分析。对此，本文提出了一种结合频率滤波器的改进型独立成分分析算法，以相关系数和峰度双重阈值为依据自动识别伪迹组件；利用眼电与脑电频率的差异，通过频率滤波器去除伪迹组件中的眼电信息，从而保留更多脑电信息。在公开数据集和本实验室数据上的实验结果表明，本文算法可以有效提升眼电伪迹去除效果，同时改善脑电信息损失，这有助于非侵入式脑-机接口的推广。.
Keywords: Electroencephalography; Filter; Independent component analysis; Non-invasive brain computer interface; Ocular artifact removal.