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

Area of Science:

• Neuroscience
• Signal Processing
• Biomedical Engineering

Background:

• Brain activity exhibits lateralization, known as asymmetry, which is crucial for understanding brain function.
• Nonlinear and nonstationary data, common in electroencephalography (EEG), pose challenges for traditional signal analysis methods.
• Accurate estimation of amplitude asymmetry in frequency is vital for neurological studies and brain-computer interface (BCI) applications.

Purpose of the Study:

• To introduce a novel method for quantifying brain activity asymmetry using an extension of empirical mode decomposition (EMD).
• To demonstrate the effectiveness of bivariate EMD (BEMD) in enhancing spectrum estimation for multichannel EEG data.
• To validate the proposed asymmetry estimation methodology through simulations and a BCI application.

Main Methods:

• Extension of the empirical mode decomposition (EMD) algorithm to a bivariate version (BEMD).
• Application of BEMD for enhanced spectrum estimation in multichannel recordings with similar signal components.
• Localized calculation of amplitude asymmetry in frequency using marginalized spectrum estimation.

Main Results:

• Bivariate EMD (BEMD) provides enhanced spectrum estimation for complex, multichannel data.
• The proposed method accurately estimates localized amplitude asymmetry in frequency.
• Simulations and a brain-computer interface (BCI) application successfully validated the asymmetry estimation technique.

Conclusions:

• The novel BEMD-based method offers a robust approach for quantifying brain activity asymmetry.
• This technique improves the accuracy of spectrum estimation in EEG analysis.
• The findings have significant implications for advancing BCI technology and neurological research.