机构地区: 华南理工大学电子与信息学院
出 处: 《华南理工大学学报(自然科学版)》 2011年第5期12-17,35,共7页
摘 要: 根据实际μ节律信号的超高斯性和不对称性分布的特点,提出了一种新的基于高阶统计量的μ节律脑电信号盲提取的不动点算法,并对该算法的局部稳定性和局部收敛性进行了讨论,给出了该算法局部稳定和局部收敛的条件.最后,利用模拟和临床脑电信号分别对该算法和FastICA算法进行了仿真分析.结果表明,对于μ节律脑电信号的提取,文中提出的算法较FastICA算法更为有效. Proposed in this paper is a new fixed-point blind extraction algorithm of μ-rhythm electroencephalogram signals based on high-order statistics,which takes into consideration the super-Guassian characteristic and non-symmetric distribution of the signals.Then,the local stability and convergence of the proposed algorithm are discussed,and the corresponding conditions are determined.Finally,the algorithm is compared with the FastICA algorithm through a case study on simulated and clinical μ-rhythm electroencephalogram signals,with a higher effectiveness of it being revealed.