Johannes P. van Dijk

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.

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 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.