KICA模型选择及其在消除脑电信号伪差中的应用

KICA Model Selection Method with Application to Removing Artifact from EEG

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.