Christian Herff

This paper introduces a synchronization method using Transcutaneous Electrical Stimulation to align LFP recordings from DBS electrodes with EEG, enabling precise and reliable data alignment across different recording modes.

This paper develops a self-correcting intracranial brain-computer interface (BCI) by integrating error-related neural activity from the insula and prefrontal cortex.

This paper decodes brain signals to detect uninformed decisions and proposes a BCI to improve decision-making quality by alerting users.

This study decodes continuous goal-directed movement from brain-wide intracranial recordings using PSID, showing widespread neural correlates across multiple brain regions.

This study evaluates brain-wide regions for speech neuroprostheses, finding that sulcal depth areas provide better decoding accuracy than the motor cortex.

This paper introduces a wireless magnetomechanical neuromodulation system that alleviates Parkinsonian symptoms in mice by stimulating mechanosensory pathways without implanted electrodes.

The paper identifies spatial and temporal distributions of semantic, articulatory, and acoustic speech representations in the brain using intracranial data from 15 participants.

The paper identifies stable global neural dynamics across participants and tasks that predict motor behavior from brain-wide recordings in humans.

The paper decodes arbitrary and informed decisions from intracranial recordings in humans, showing differences in spatio-temporal patterns between the two decision types.

The paper analyzes intracranial recordings to compare speech activity detection across overt, mouthed, and imagined speech, finding a nested hierarchy of relevant brain channels.

T-Rex automates neural recording experiments with a single button press, enabling flexible, platform-independent data collection.

The paper provides a dataset of intracranial EEG recordings from 10 participants during speech production to study neural mechanisms and develop speech decoding technologies.

This paper decodes executed and imagined grasping movements from non-motor brain areas using a lower dimensional representation, achieving high accuracy without primary motor cortex signals.

This study uses wrist-worn accelerometers to classify bradykinesia fluctuations in Parkinson's patients at the single-minute timescale using individual and group models.

This paper develops a real-time speech synthesis system from neural activity for imagined and whispered speech, using implanted electrodes and frontal lobe activity.

The paper shows that high-gamma ECoG activity in the prefrontal cortex tracks musical rhythm periodicity during perception and imagination.

This paper uses deep neural networks to reconstruct speech from ECoG signals, achieving high correlation between reconstructed and original logMel spectrograms.