Kai Joshua Miller

The paper uses electrical stimulation to map directional connectivity in human visual cortex, revealing an asymmetric architecture of interareal influence.

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.

The paper measures conduction velocities in human white matter bundles using dMRI tractography and intracranial stimulation, finding high variability and a predominance of slow connections.

This study shows that single electrical pulses in white matter modulate visual evoked potentials but not broadband responses, suggesting additive noise effects and enhanced visual input synchrony.

This study investigates how attention modulates prediction-error (PE) encoding in thalamic and cortical circuits during learning, revealing distinct informational dynamics and reorganization of PE representations.

This paper develops a porcine model of mesial temporal lobe epilepsy using kainic acid and an implantable device to study seizures and electrophysiological features.

The paper investigates the roles of broadband and narrowband gamma oscillations in visual information encoding using ECoG and LFP data from humans and macaques, finding broadband signals to be synergistic and narrowband gamma to be redundant.

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.

The paper compares re-referencing and spectral analysis techniques to estimate BSEP source locations in sEEG data from human subjects.

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

The paper identifies reference contamination in single pulse electrical stimulation data and provides strategies to detect and mitigate it through re-referencing or data recollection.

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.

This study shows that electrical stimulation of different pulvinar subregions in epilepsy patients evokes distinct patterns of brain activity in occipital, parietal, and temporal areas.

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.

This study maps the brain-wide network activated by NAc DBS in mice and finds similar connectivity patterns in humans, suggesting conserved mechanisms across species.

This study develops a functional brain mapping technique using task-based electrophysiological changes in sEEG to identify movement and speech areas in epilepsy patients.

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.

The paper investigates whether cortical interactions during prediction error processing are synergistic or redundant, finding that both ERPs and broadband signals show high synergy across the cortical hierarchy.

The paper develops a method to detect cycles in sparse, irregularly sampled neuro-behavioral timeseries using basis pursuit denoising with polynomial detrending, validated on epileptiform discharge data.

This paper introduces a flexible multi-step hypothesis testing method using cluster-based permutation tests with LMEs and GLMEs to analyze human ECoG data, improving statistical power and accounting for random effects.

ER-detect automates the detection of early evoked responses in iEEG data, achieving performance comparable to inter-rater reliability.

The paper identifies unique stimulation-evoked waveforms in distinct limbic subsystems using intracranial recordings, suggesting potential biomarkers for limbic network function.

This paper introduces a method to quantify and compare brain stimulation response shapes using canonical response parameterization, enabling statistical analysis of single-pulse intracranial electrical stimulation data.

This study uses electrical stimulation to identify distinct electrophysiological responses in the human ventral temporal cortex, revealing different input connectivity profiles from cortical and limbic regions.

The paper describes a system for chronic stimulation and recording of thalamic-hippocampal theta activities in humans during memory tasks, showing their correlation with memory performance and modulation by deep brain stimulation.

The paper identifies a motor association area in the central sulcus that interrupts the topological map of body movements generated by the precentral gyrus.

The paper develops a system integrating implantable brain stimulation devices with wearable electronics for adaptive stimulation and real-time analysis in natural environments.

This study shows that neuronal communication speed increases with age, reaching peak efficiency in middle age, across both long- and short-range brain connections.