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2022-12-15T03:34:48Z
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Signal and Noise Covariance Estimation Based on ICA for High-Resolution Cortical Dipole Imaging
Hori, Junichi
6345
Sunaga, Kentarou
6346
Watanabe, Satoru
6347
EEG
cortical dipole imaging
spatial inverse filter
covariance
independent component analysis
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.
journal article
電子情報通信学会
2010-09
application/pdf
IEICE transactions on information and systems
9
E93-D
2626
2634
AA10826272
0916-8532
https://niigata-u.repo.nii.ac.jp/record/1945/files/E93-D_9_2626-2634.pdf
eng
http://doi.org/10.1587/transinf.e93.d.2626
http://search.ieice.org/
Copyright (C) 2010 IEICE
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