摘要
事件相关诱发电位信号的稳健提取一直是脑电信号处理领域的难题。独立分量分析算法是一种盲源分离技术,主要解决独立源的二维线性混合问题。文章设计了1组峭度不同的仿真脑电信号,采用扩展信息最大的独立分量分析算法提取仿真诱发电位信号。实验结果表明,仿真诱发电位信号分离前后的峭度接近,相关系数大于0.99,且分离后的诱发电位信号基本保持了原来波形的特征,能有效地将混合在诱发电位信号中的自发脑电信号、肌电干扰及工频干扰等信号分离开来,实现了微弱的诱发电位信号在强噪声中的有效提取,为真实事件相关诱发电位信号的提取提供了思路。
The robust extraction of Event-related evoked potential signal （ERPs） is always a difficult problem in electroencephalogram （EEG） signal processing. Independent component analysis （ICA） is a blind source separation technique that has been developed to solve a 2D linear mixing problem of independent sources. In this paper, a group of simulated EEG signal with different kurtosis was designed, and the simulated ERPs were extracted by extended Infomax ICA algorithm. The simulation results showed that the kurtosis of the separated ERPs was similar with the source ERPs; the correlation coefficient was 〉 0.99 and the separated ERPs remained the waveform features of source signal. ICA can separate the EEG signal, electromyography signal and power frequency interferential signal, which are mixed in the ERP signal. Extended Infomax ICA algorithm can extract the weak ERPs from strong background artifacts efficiently, which provides a good idea for extracting real ERPs.
出处
《中国组织工程研究与临床康复》
CAS
CSCD
北大核心
2009年第17期3265-3267,共3页
Journal of Clinical Rehabilitative Tissue Engineering Research
基金
北京师范大学认知神经科学与学习国家重点实验室开放课题资助项目
北京师范大学应用实验心理北京市重点实验室开放研究基金课题资助项目
江苏省青蓝工程资助项目~~
