Ziv M. Williams

This paper introduces a multi-user transformer-based decoder for intracortical speech BCIs, achieving lower word error rates with less training data than individual models.

The paper shows that during typing skill acquisition using a BCI, neural population dynamics in the dorsal premotor cortex become more compact, with left hemisphere showing enhanced firing-rate modulation and generalization.

The paper determines optimal electrode spacing for maximizing single unit yield in neural recordings across species and brain regions.

This paper demonstrates cross-brain transfer learning for intracortical BCIs decoding speech and handwriting, showing improved performance with limited training data and specific decoder design.

This study analyzes neural activity in the precentral gyrus of two spinal cord injury patients to classify hand gestures with 70% accuracy, showing potential for intracortical BCI applications.

This paper decodes intended speech from a person with anarthria and locked-in syndrome using an intracortical brain-computer interface, showing that phonemes, words, and higher-order language units can be decoded above chance.

This paper introduces NoMAD, a method that stabilizes brain-computer interfaces by aligning latent dynamics, achieving stable decoding without supervised recalibration over long periods.

This study uses deep learning models to analyze neural activity during conversation, showing distributed and context-dependent patterns for speech production and comprehension.

This study shows that the human hippocampus can process oddball tones and language stimuli, including semantic and grammatical features, even under general anesthesia.

DREDge improves motion correction in high-density extracellular recordings across species, enhancing reliability and reducing errors in electrophysiological data.

This study decodes lexical semantic information from natural speech using intracranial EEG in participants during spontaneous conversation, achieving 21% accuracy.

The paper maps human motor cortex using microelectrode recordings, finding whole-body representations and challenging the traditional motor homunculus model.

This study investigates how theta-burst stimulation affects neural activity in human brain networks using intracranial EEG during direct electrical stimulation.

The paper introduces a high-channel-count flexible electrode for deep brain recording, capable of capturing single-unit activity from up to 128 channels.

This study shows that co-occurring ripple oscillations enhance neuronal interactions between cortical locations in humans by facilitating prediction and co-firing of neurons.

This paper introduces DREDge, an algorithm for robust online drift estimation in electrophysiology data, improving scalability and temporal resolution for human and mouse recordings.

This study examines ketamine-induced brain dynamics in humans using intracranial recordings, identifying oscillatory changes linked to antidepressant and dissociative effects.

This paper describes a new high-density Neuropixels probe enabling single-cell resolution recordings from over 200 cortical neurons in humans during surgery, identifying eight distinct single unit classes.

The paper examines how stimulation location relative to grey and white matter affects single pulse electrical stimulation responses in the human brain, finding location and parameter-specific effects.

The paper identifies three distinct classes of cortical activity using high-density microelectrodes, showing they are modulated by stimuli and present across species.

This study uses state-space modeling and intracranial stimulation to modulate hidden emotional states during conflict resolution, showing bidirectional effects on behavior.