A Resampling Test for the Total Independence of Stationary Time Series: Application to the Performance Evaluation of ICA Algorithms

被引：0
作者：
Juha Karvanen
机构：
[1] RIKEN Brain Science Institute,Laboratory for Advanced Brain Signal Processing
[2] National Public Health Institute,Department of Epidemiology and Health Promotion
来源：
Neural Processing Letters | 2005年 / 22卷
关键词：
EEG; independence; independent component analysis; multivariate time series;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
This paper addresses the independence testing of stationary time series. We develop a resampling test based on the Kankainen–Ushakov test of total independence. The resampling test, contrary to the original test, can be also applied to the data with a time-structure. The simulation studies demonstrate the good performance of the proposed test even with strongly autocorrelated time series. As an application, we consider biomedical signal processing and independent component analysis (ICA). The independence test can be used as a performance criterion for ICA algorithms. The practical example of performance evaluation deals with the ICA of electroencephalogram (EEG) data.
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页码：311 / 324
页数：13
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