Application of State-Space Modeling to Instantaneous Independent-Component Analysis

摘要：

In this paper, we design an algorithm for decomposing multivariate electroencephalographic (EEG) time series into independent components, based on Independent-Component Analysis (ICA) and State-Space Modeling (SSM). We aim at combining the strong aspects of both methods: ICA provides an initial model for SSM which is then further optimized by maximum-likelihood. We also propose an approach for augmentation of the state space by extracting additional components from the data prediction errors. The estimate of the mixing matrix provided by ICA is excluded from optimization. Practical application of the proposed algorithm is demonstrated by an example of the analysis of EEG data recorded from an epilepsy patient.

关键词： ICA SSM ARMA EEG analysis

作者: Alina Santillan-Guzman Ulrich Heute Andreas Galka Ulrich Stephani

作者单位: Faculty of Engineering Christian-Albrechts-University of Kiel Kiel, Germany Department of Neuropediatrics Christian-Albrechts-University of Kiel Kiel, Germany

会议类型: 国际会议

会议名称: 2011 4th International Conference on Biomedical Engineering and Informatics(第四届生物医学工程与信息学国际会议 BMEI 2011)

会议地点: 上海

会议语种:英文

页码: 638-641

在线出版日期: 2011-10-15（万方平台首次上网日期，不代表论文的发表时间）