Ksenia Germanova

This paper introduces a canonical correlation approach for multi-channel cardiac artefact removal in EEG data and shows it outperforms traditional methods in reducing artefacts while preserving neural signals.

This study investigates how cardiac signals influence the perception of motor intention during Libet's task, finding that movement initiation aligns with the diastolic phase of the cardiac cycle.

This study uses MEG and hierarchical Bayesian modeling to show that euthymic bipolar disorder patients anticipate environmental volatility but fail to learn effectively, with altered neural activity in prefrontal regions.