Time-frequency analysis of EEG asymmetry using bivariate empirical mode decomposition

C. Park; D. Looney; P. Kidmose; Michael Ungstrup; D.P. Mandic

doi:10.1109/TNSRE.2011.2116805

Time-frequency analysis of EEG asymmetry using bivariate empirical mode decomposition

C. Park, D. Looney, P. Kidmose, Michael Ungstrup, D.P. Mandic

Research output: Contribution to journal/Conference contribution in journal/Contribution to newspaper › Journal article › Research › peer-review

81 Citations (Scopus)

Abstract

A novel method is introduced to determine asymmetry, the lateralization of brain activity, using extension of the algorithm empirical mode decomposition (EMD). The localized and adaptive nature of EMD make it highly suitable for estimating amplitude information across frequency for nonlinear and nonstationary data. Analysis illustrates how bivariate extension of EMD (BEMD) facilitates enhanced spectrum estimation for multichannel recordings that contain similar signal components, a realistic assumption in electroencephalography (EEG). It is shown how this property can be used to obtain a more accurate estimate of the marginalized spectrum, critical for the localized calculation of amplitude asymmetry in frequency. Simulations on synthetic data sets and feature estimation for a brain-computer interface (BCI) application are used to validate the proposed asymmetry estimation methodology.

Original language	English
Journal	I E E E Transactions on Neural Systems and Rehabilitation Engineering
Volume	19
Issue	4
Pages (from-to)	366-373
Number of pages	8
ISSN	1534-4320
DOIs	https://doi.org/10.1109/TNSRE.2011.2116805
Publication status	Published - 1 Aug 2011

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10.1109/TNSRE.2011.2116805

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abstract = "A novel method is introduced to determine asymmetry, the lateralization of brain activity, using extension of the algorithm empirical mode decomposition (EMD). The localized and adaptive nature of EMD make it highly suitable for estimating amplitude information across frequency for nonlinear and nonstationary data. Analysis illustrates how bivariate extension of EMD (BEMD) facilitates enhanced spectrum estimation for multichannel recordings that contain similar signal components, a realistic assumption in electroencephalography (EEG). It is shown how this property can be used to obtain a more accurate estimate of the marginalized spectrum, critical for the localized calculation of amplitude asymmetry in frequency. Simulations on synthetic data sets and feature estimation for a brain-computer interface (BCI) application are used to validate the proposed asymmetry estimation methodology.",

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Time-frequency analysis of EEG asymmetry using bivariate empirical mode decomposition. / Park, C.; Looney, D.; Kidmose, P. et al.
In: I E E E Transactions on Neural Systems and Rehabilitation Engineering, Vol. 19, No. 4, 01.08.2011, p. 366-373.

Research output: Contribution to journal/Conference contribution in journal/Contribution to newspaper › Journal article › Research › peer-review

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Time-frequency analysis of EEG asymmetry using bivariate empirical mode decomposition

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