title: Time-frequency analysis of EEG asymmetry using bivariate empirical mode decomposition.
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.
authors.affiliation: Department of Electrical and Electronic Engineering, Imperial College London, SW7 2BT London, UK.
authors.forename: Cheolsoo
authors.initials: C
authors.lastname: Park
authors.forename: David
authors.initials: D
authors.lastname: Looney
authors.forename: Preben
authors.initials: P
authors.lastname: Kidmose
authors.forename: Michael
authors.initials: M
authors.lastname: Ungstrup
authors.forename: Danilo P
authors.initials: DP
authors.lastname: Mandic
mesh_terms: D000465:Algorithms; D001921:Brain / Q000502:physiology*; D003071:Cognition / Q000502:physiology; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D004569:Electroencephalography / Q000379:methods* / Q000706:statistics & numerical data; D007839:Functional Laterality / Q000502:physiology*; D006801:Humans; D007092:Imagination / Q000502:physiology; D017711:Nonlinear Dynamics; D011597:Psychomotor Performance / Q000502:physiology; D012399:Rotation; D059629:Signal-To-Noise Ratio; D014584:User-Computer Interface