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目的利用独立分量分析方法(ICA)将混合在观测信号中相互独立的源信号分离出来。方法记录3个正常人自然眨眼和水平扫视条件下7道脑电信号和2道眼电信号,选取7道脑电信号进行处理,2道眼电信号用来指示干扰源的情况。使用扩展相似对角化算法(JADE)将脑电信号分解成多个独立分量,同时利用伪迹脑地形图特征,判断出与眼电伪迹相关分量并将其去除。结果存在于前额电极的眼电干扰被消除,同时其他电极上的信号细节成分较好地保留下来。独立分量分析方法成功去除了脑电信号中的眼电伪迹。结论本文中算法可以用于实现脑电信号中眨眼和水平扫视干扰的去除,其中对前者的去除更加有效。
Objective To separate source signals that are independent of each other in the observed signal using the Independent Component Analysis (ICA) method. Methods Seven EEG signals and two EOG signals were recorded in three normal subjects under natural blinking and horizontal scanning conditions. Seven EEG signals were selected for processing and two EOG signals were used to indicate the source of interference. The extended similar diagonalization algorithm (JADE) was used to decompose the EEG into multiple independent components. At the same time, the artifacts associated with the artifacts of the electrooculogram were determined and removed by using the features of the artifacts. As a result, the ocular electrical interference present on the forehead electrode is eliminated, while the signal details on other electrodes are well preserved. Independent component analysis successfully removes the electro-oculogram artifacts in EEG signals. Conclusion The algorithm in this paper can be used to remove the blinking and panning disturbances in the EEG, in which the removal of the former is more effective.