Blind Source Separation Based on ICA

摘要：

Independent Component Analysis (ICA) is a new technique to statistically extract independent components from the observed multidimensional mixture of data. Many successful examples of ICA application in the filed of biomedical signal processing are reported recently. In this paper, a steepest descent algorithm of ICA is proposed. A pre-whiten procedure is performed to decorrelating the sensor (mixed) signals before extracting vector. The proposed method is tested with simulated signals and clinical EEG.

关键词： Independent Component Analysis (ICA) Electroencephalograph (EEG) The steepest descent algorithm.

作者: Zhou Weidong Jia Lei Li Yingyuan

作者单位: School of Information Science, Shandong University, China School of Control Science, Shandong University, China

会议类型: 国际会议

会议名称: 8th International Conference on Neural Information Processing(ICONIP 2001)(第八届国际神经信息处理大会)

会议地点: 上海

会议语种:英文

页码: 1233-1236

在线出版日期: 2001-11-14（万方平台首次上网日期，不代表论文的发表时间）