摘要
理论分析并结合实验验证指出基于正定核的独立分量分析算法(K ICA)的优化与分离性能与其模型参数的选择有关。提出了一种简单高效的模型选择方法:在混合信号中附加一个已知验证信号,通过最小化该已知信号的分离误差来选择最优模型参数。实验结果表明:经模型选择后的K ICA能成功分离脑电信号中的心电伪差。
The impact of model parameters of KICA on its optimization and separation performances is analyzed in theoretical aspects and demonstrated with experiments. In order to select proper model param eters, a time efficient model selection method is put forward, which annexes a simple known signal to the sensor signals and gets optimal model parameter setting by minimizing the separation error. KICA with model selection step is applied to the task of removing ECG artifact from the EEG signal and the result shows KICA works effectively.
出处
《华东理工大学学报（自然科学版）》
EI
CAS
CSCD
北大核心
2006年第4期456-461,483,共7页
Journal of East China University of Science and Technology
基金
国家重点基础研究发展规划项目(2002CB312200)
教育部高校博士点基金项目(20040251010)
国家自然科学基金项目(69974014)
