Papers

Peer-reviewed
Sep, 2010

Signal and Noise Covariance Estimation Based on ICA for High-Resolution Cortical Dipole Imaging

IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS
  • Junichi Hori
  • ,
  • Kentarou Sunaga
  • ,
  • Satoru Watanabe

Volume
E93D
Number
9
First page
2626
Last page
2634
Language
English
Publishing type
Research paper (scientific journal)
DOI
10.1587/transinf.E93.D.2626
Publisher
IEICE-INST ELECTRONICS INFORMATION COMMUNICATIONS ENG

We investigated suitable spatial inverse filters for cortical dipole imaging from the scalp electroencephalogram (EEG). The effects of incorporating statistical information of signal and noise into inverse procedures were examined by computer simulations and experimental studies. The parametric projection filter (PPF) and parametric Wiener filter (PWF) were applied to an inhomogeneous three-sphere volume conductor head model. The noise covariance matrix was estimated by applying independent component analysis (ICA) to scalp potentials. The present simulation results suggest that the PPF and the PWF provided excellent performance when the noise covariance was estimated from the differential noise between EEG and the separated signal using ICA and the signal covariance was estimated from the separated signal. Moreover, the spatial resolution of the cortical dipole imaging was improved while the influence of noise was suppressed by including the differential noise at the instant of the imaging and by adjusting the duration of noise sample according to the signal to noise ratio. We applied the proposed imaging technique to human experimental data of visual evoked potential and obtained reasonable results that coincide to physiological knowledge.

Link information
DOI
https://doi.org/10.1587/transinf.E93.D.2626
Web of Science
https://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=JSTA_CEL&SrcApp=J_Gate_JST&DestLinkType=FullRecord&KeyUT=WOS:000282245100028&DestApp=WOS_CPL
ID information
  • DOI : 10.1587/transinf.E93.D.2626
  • ISSN : 0916-8532
  • Web of Science ID : WOS:000282245100028

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