Nuri F. Ince

The paper develops and validates an open-source ecosystem for implantable brain-computer interfaces and adaptive neuromodulation, demonstrating its capabilities through preclinical and human studies.

This paper introduces a novel stereotactic coordinate system for the insula to improve surgical targeting and outcome assessment by accounting for individual anatomical variability.

This study evaluates stereo EEG-based brain-computer interfacing (BCI) in adults and children, finding that overt motor BCI is more effective than imagery BCI, with performance varying by age and cortical region.

KenazLBM is a model that characterizes and predicts brain states from raw iEEG data, generalizing across subjects with heterogeneous recording channels.

The paper introduces a method to parameterize and cluster single-unit action potentials from DBS microelectrode recordings to improve lead placement in deep brain stimulation.

The paper maps motor execution representation in the human insula, showing somatotopic organization and effector-specific connectivity to primary motor cortex.

The paper analyzes ECoG signals during different hand movements to understand cortical activity patterns and their spatial overlap, finding that pinching shows higher overlap with index finger flexion.

The paper implants a neuromodulation platform in a beagle to study brain activity, finding the anterior ectosylvian gyrus responds selectively to social reinforcement, suggesting speech understanding may evolve from social brain structures.

This study uses unsupervised clustering to identify distinct neuronal firing patterns in the subthalamic nucleus of Parkinson's disease patients and finds spatial differences in firing activity.