摘要
脑电信号是一种随机性与非平稳性非常强的信号,在观察与研究的过程中极易受到噪声的影响,特别是眼电信号的产生严重干扰到脑电信号。为解决上述问题,对采集来的源信号先进行消躁处理,用来减少实验误差是必要的。在传统独立成分分析算法的基础上,结合权值迭代公式和偏差调制公式进行第二次估计,得到比第一次独立成分分析分离更精确逼近信号源的结果,从而达到优化独立成分分析算法的目的。结果表明,与传统方法相比,优化的独立成分分析得到的信号更纯净,更好的去除了眼电信号,可准确提取脑电信号。
EEG signal is the one with strong random and non-stationary noises. In the process of bservation and study, EEG signal is easy to be influenced by other noises, especially by the Electro-Oculogram（EOG）. Therefore, it is necessary to reduce the experimental error for the acquisition of the source signal. On the basis of the traditional independent component analysis algorithm, the second time estimation was carried out by combining the weight iteration formula with the deviation modulation formula. The results of the more accurate approximation of the signal, source were obtained than the first independent component analysis. The results show that, compared with the traditional method, the signals based on the optimization of independent component analysis are purer, and the EOG can be well eliminated.
出处
《计算机仿真》
北大核心
2017年第1期364-367,422,共5页
Computer Simulation
