Marius Klug

This paper demonstrates real-time EEG decoding of interaction intent during dynamic VR gameplay using eye gaze and passive BCIs, showing above-chance accuracy in classifying approach-avoidance and affordance-related decisions.

HArtMuT improves M/EEG signal localization by modeling eye and muscle contributions, achieving comparable accuracy to brain sources.

Open-source MATLAB pipeline (EEGLAB, fieldtrip) that synchronizes mobile EEG with motion and eye-tracking data, extracts gait, blink and heartbeat events, and clusters independent components per region of interest.

The paper evaluates the impact of movement intensity and automatic sample rejection on ICA decomposition quality across multiple EEG datasets, finding that moderate cleaning improves decomposition regardless of movement intensity.

Zapline-plus automatically and adaptively removes frequency-specific noise from M/EEG and LFP data by segmenting data, detecting noise peaks, and adjusting parameters for optimal cleaning.

The paper uses mobile EEG to study landmark-based navigation in virtual reality, identifying brain areas and neural modulations involved in spatial learning and behavior.

This study investigates human cortical dynamics during full-body heading changes using Mobile Brain/Body Imaging, finding distinct neural responses in the retrosplenial complex during physical versus visual rotation.

This study evaluates how preprocessing parameters affect ICA decomposition of EEG data in mobile and stationary experiments, finding that higher high-pass filters and more channels improve results in mobile settings.

This paper introduces a Bayesian framework that unifies EEG data cleaning, source separation, and imaging, showing improved performance over existing methods.