Klaus Gramann

This study examines how urban and natural features in environments affect subjective experience and neural responses using EEG, finding that greenery correlates with positive perception and specific brain activity patterns.

This study shows that scalp EEG can detect hexadirectional modulation of gamma band activity in the MTL, linked to spatial navigation and memory retrieval.

This study shows that older adults have worse navigation skills due to changes in their gait and brain activity, and they rely more on visual landmarks, which is less effective than in younger adults.

This study investigates how humans use landmarks to correct spatial errors during navigation, finding that theta-band activity in the RSC supports this process.

This study shows that attentional cueing effects reverse during locomotion through curved corridors, with faster responses to incongruent stimuli due to dynamic environmental and motor demands.

The paper investigates how real-world contexts modulate neural oscillations using mobile EEG and augmented reality, finding differences in ERPs and theta-band power between congruent and incongruent object environments.

The paper examines affordance perception by comparing early perceptual and late motor-related ERP components in different movement contexts, finding that affordance perception shifts from automated to context-dependent over time.

This paper reviews Neurourbanism studies using mobile EEG, identifies methodological flaws, and provides guidelines for better practice.

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.

This study shows that using landmark-based navigation instructions improves incidental spatial learning in real-life environments compared to standard instructions.

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 shows that alpha-band oscillations in specific brain regions correlate with architectural affordances, influencing sensorimotor dynamics during interaction with the environment.

This study shows that adding landmarks to navigation instructions improves spatial knowledge acquisition even with delays and varying detail levels.

This study investigates how hand movement velocity affects cognitive conflict processing in a 3D virtual reality object selection task, finding that velocity modulates brain responses and induces a prediction error negativity.

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 investigates brain dynamics during dual-task walking using VR and MoBI, finding reduced P3 amplitude and changes in PSDs despite no performance impact.

The paper shows that mobile EEG can capture brain activity related to real-world navigation by extracting saccade- and blink-related potentials and gait activity, even in complex environments.

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

The paper investigates how brain activity reflects architectural affordances by analyzing sensory and motor responses in virtual environments.

This study examines if P300 brain dynamics observed in lab settings are consistent in real-world driving scenarios.

SEREEGA generates simulated EEG data with known ground truth to evaluate signal processing and machine learning methods.

This study shows that highlighting landmarks in navigation instructions enhances long-term spatial memory and EEG correlates of recollection.

The paper investigates spatial learning through interactive virtual environments, finding that exploration behavior changes correlate with improved sketch map quality.