Long-term Intracortical Neural activity and Kinematics (LINK): An intracortical neural dataset for chronic brain-machine interfaces, neuroscience, and machine learning

Hisham Temmar, Yixuan Wang, Nina Gill, Nicholas Mellon, Chang Liu, Luis Hernan Cubillos, Rio I. Parsons, Joseph T Costello, Matteo Ceradini, Madison M. Kelberman, Matthew Mender, Aren Hite, Dylan Michael Wallace, Samuel R. Nason-Tomaszewski, Parag Ganapati Patil, Matt Willsey, Anne Draelos, Cynthia Chestek

Keywords: dataset, brain-machine interfaces, neural data, neural decoding, temporal, stability, finger movements

TL;DR: A dataset containing neural activity and finger kinematics from 303 sessions of a monkey performing a 2-DOF finger movement task, recorded over a 1242 day (~3.5 year) timespan.

Abstract: Intracortical brain-machine interfaces (iBMIs) have enabled movement and speech in people living with paralysis by using neural data to decode behaviors in real-time. However, intracortical neural recordings exhibit significant instabilities over time, which poses problems for iBMIs, neuroscience, and machine learning. For iBMIs, neural instabilities require frequent decoder recalibration to maintain high performance, a critical bottleneck for real-world translation. Several approaches have been developed to address this issue, and the field has recognized the need for standardized datasets on which to compare them, but no standard dataset exists for evaluation over year-long timescales. In neuroscience, a growing body of research attempts to elucidate the latent computations performed by populations of neurons. Nonstationarity in neural recordings imposes significant challenges to the design of these studies, so a dataset containing recordings over large time spans would improve methods to account for instabilities. In machine learning, continuous domain adaptation of temporal data is an area of active research, and a dataset containing shift distributions on long time scales would be beneficial to researchers. To address these gaps, we present the LINK Dataset (Long-term Intracortical Neural activity and Kinematics), which contains intracortical spiking activity and kinematic data from 312 sessions of a non-human primate performing a dexterous, 2 degree-of-freedom finger movement task, spanning 1,242 days. We also present longitudinal analyses of the dataset’s neural spiking activity and its relationship to kinematics, as well as overall decoding performance using linear and neural network models. The LINK dataset (https://dandiarchive.org/dandiset/001201) and code (https://github.com/chesteklab/LINK_dataset) are freely available to the public.

Dataset URL: https://dandiarchive.org/dandiset/001201/0.251023.2336

Code URL: https://github.com/chesteklab/LINK_dataset

Primary Area: Data and Benchmarking scenarios in Neuroscience and cognitive science (e.g., neural coding, brain-computer interfaces)

Submission Number: 2051