Felix Blankenburg

The paper investigates tactile long-term memory retrieval, finding increased somatosensory cortex and hippocampal activity during successful recognition of learned vibrotactile patterns.

This study shows that online anodal tDCS of the left PPC improves tactile working memory accuracy over time, with larger benefits for lower baseline performers.

The paper decodes grip-force intensity from fMRI data, showing a shift from abstract to effector-specific neural codes before movement execution.

This paper develops a computational framework to standardize tDCS stimulation doses across brain regions while preserving individual variability, validated in an independent sample.

The paper compares ERP components predicting conscious perception under tactile vs. electrical somatosensory stimulation, finding P50 predicts tactile detection but not electrical.

This study uses fMRI and MVPA to decode how grip-force intensity is represented in the brain during working memory, showing distinct patterns in vmPFC and motor areas.

The paper uses fMRI and Bayesian modeling to show that premotor cortex and intraparietal sulcus encode perceptual decisions independently of motor actions and sensory modalities.

This study investigates probabilistic inference across multiple sensory modalities using EEG mismatch responses in a tri-modal paradigm, finding comparable signatures and cross-modal predictive sensitivity.

This study uses fMRI to investigate how the brain encodes working memory of biological motion as self-associated versus other-associated, identifying specific brain regions involved in self-other distinction during movement processing.

The paper investigates how the human amygdala processes aversive stimuli on artificial limbs using fMRI in VR, finding enhanced amygdala activation for aversive stimuli with stronger multisensory integration.

The paper compares two event-related potentials in a somatosensory task to determine if they reflect perception or task demands, finding that the P300 is task-dependent and not a reliable marker of awareness.

This study investigates Bayesian learning in somatosensory processing using EEG data, finding distinct neural signatures for model inadequacy and updating.

The paper identifies brain regions that represent abstract choices in vibrotactile comparisons, independent of sensorimotor components, using fMRI-MVPA and a modified task.