Alexander Moiseev

The paper introduces a source-level noise covariance model for resting-state MEG/EEG data, improving source reconstruction reliability by avoiding artificial baseline distortions.

This paper introduces SMCMV beamformers that combine MUSIC and MCMV localizers for improved source localization in MEG/EEG, showing better performance in non-ideal conditions.

This paper describes a method to generate multi-epoch brain signals with predefined mutual correlations, including evoked and oscillatory components.

This study uses MEG to map brain activation dynamics during naturalistic audiovisual stimuli, showing distinct patterns for visual and auditory stimuli classes.

This study compares MCMV and LCMV beamformers for estimating electrophysiological connectivity, showing MCMV reduces signal leakage and preserves short lag interactions.