arXiv subjects

David M. Brandman

Publications and source records attributed to David M. Brandman.

4 recordsLinked to original sources

arXiv2026

A Personalized and Adaptable User Interface for a Speech and Cursor Brain-Computer Interface

Communication and computer interaction are important for autonomy in modern life. Unfortunately, these capabilities can be limited or inaccessible for the millions of people living with paralysis. While implantable brain-computer interfaces (BCIs) show promise for restoring these capabilities, little has been explored on designing BCI user interfaces (UIs) for sustained daily use. Here, we present a personalized UI for an intracortical BCI system that enables users with severe paralysis to communicate and interact with their computers independently. Through a 22-month longitudinal deployment with one participant, we used iterative co-design to develop a system for everyday at-home use and documented how it evolved to meet changing needs. Our findings highlight how personalization and adaptability enabled independence in daily life and provide design implications for developing future BCI assistive technologies.

cs.HC↗

arXiv2026

High-Density Multi-Depth Human Recordings Using 45 mm Long Neuropixels Probes

Neuropixels probes, initially developed for use in small animal models, have transformed basic neuroscience by enabling high-density, single-cell resolution recordings across multiple brain regions simultaneously. The recent development of Neuropixels 1.0 NHP Long, a longer probe designed for non-human primates, has expanded this capability, enabling unprecedented simultaneous access to multiple cortical layers and deep brain structures of large-brained animals. Here, we report the first use of these probes in humans, aiming to establish safe intraoperative use and assess feasibility for clinical and research applications. Nine patients undergoing neurosurgical procedures, including epilepsy or tumor resection and deep brain stimulation (DBS) implantation, were enrolled. Successful intraoperative recordings were obtained from surface and deep cortical structures without probe breakage or adverse events. Compared with conventional electrodes, the Neuropixels probe enabled dense sampling across multiple parenchymal depths with submillisecond temporal resolution. Recordings were obtained from deep targets including the hippocampus and cingulate cortex, as well as from regions that are challenging to access with single-unit precision, such as the superior frontal sulcus. Custom tools and refined workflows lowered technical barriers for operative use and improved recording stability. Neural activity was observed across all recordings. Neuropixels 1.0-NHP Long probes can be deployed in the human operating room, enabling simultaneous recordings from multiple brain structures at single-neuron resolution. These methods expand opportunities for studying human brain function and pathology in vivo, and may ultimately support the development of more precise neurosurgical interventions.

q-bio.NC↗

arXiv2018

BCI decoder performance comparison of an LSTM recurrent neural network and a Kalman filter in retrospective simulation

Intracortical brain computer interfaces (iBCIs) using linear Kalman decoders have enabled individuals with paralysis to control a computer cursor for continuous point-and-click typing on a virtual keyboard, browsing the internet, and using familiar tablet apps. However, further advances are needed to deliver iBCI-enabled cursor control approaching able-bodied performance. Motivated by recent evidence that nonlinear recurrent neural networks (RNNs) can provide higher performance iBCI cursor control in nonhuman primates (NHPs), we evaluated decoding of intended cursor velocity from human motor cortical signals using a long-short term memory (LSTM) RNN trained across multiple days of multi-electrode recordings. Running simulations with previously recorded intracortical signals from three BrainGate iBCI trial participants, we demonstrate an RNN that can substantially increase bits-per-second metric in a high-speed cursor-based target selection task as well as a challenging small-target high-accuracy task when compared to a Kalman decoder. These results indicate that RNN decoding applied to human intracortical signals could achieve substantial performance advances in continuous 2-D cursor control and motivate a real-time RNN implementation for online evaluation by individuals with tetraplegia.

cs.HC↗

arXiv2016

The discriminative Kalman filter for nonlinear and non-Gaussian sequential Bayesian filtering

The Kalman filter (KF) is used in a variety of applications for computing the posterior distribution of latent states in a state space model. The model requires a linear relationship between states and observations. Extensions to the Kalman filter have been proposed that incorporate linear approximations to nonlinear models, such as the extended Kalman filter (EKF) and the unscented Kalman filter (UKF). However, we argue that in cases where the dimensionality of observed variables greatly exceeds the dimensionality of state variables, a model for $p(\text{state}|\text{observation})$ proves both easier to learn and more accurate for latent space estimation. We derive and validate what we call the discriminative Kalman filter (DKF): a closed-form discriminative version of Bayesian filtering that readily incorporates off-the-shelf discriminative learning techniques. Further, we demonstrate that given mild assumptions, highly non-linear models for $p(\text{state}|\text{observation})$ can be specified. We motivate and validate on synthetic datasets and in neural decoding from non-human primates, showing substantial increases in decoding performance versus the standard Kalman filter.

stat.ML↗