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

Author: Karvanen, Juha¹

Source: Neural Processing Letters, Volume 22, Number 3, December 2005 , pp. 311-324(14)

Publisher: Springer

Key:

- Free Content

- New Content

- Subscribed Content

- Free Trial Content

Abstract:

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.

Keywords: EEG; independence; independent component analysis; multivariate time series

Document Type: Research article

DOI: 10.1007/s11063-005-0956-0

Affiliations: 1: Laboratory for Advanced Brain Signal Processing, RIKEN Brain Science Institute, Japan, Email: juha.karvanen@ktl.fi

The full text electronic article is available for purchase. You will be able to download the full text electronic article after payment.

$47.00 plus tax Refund Policy