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

Yixuan Wang (University of Michigan - Ann Arbor) · Chang Liu (Peking University) · Hisham Temmar (University of Michigan - Ann Arbor) · Nina Gill (University of Michigan - Ann Arbor) · Nicholas Mellon (University of Michigan - Ann Arbor) · Luis Cubillos (University of Michigan) · Rio Parsons (University of Michigan - Ann Arbor) · Joseph Costello (University of Michigan - Ann Arbor) · Matteo Ceradini (Sant'Anna School of Advanced Studies) · Madison Kelberman (University of Michigan - Ann Arbor) · Matthew Mender (University of Michigan - Ann Arbor) · Aren Hite (University of Michigan - Ann Arbor) · Dylan Wallace (University of Michigan - Ann Arbor) · Samuel Nason-Tomaszewski (Emory University) · Parag Patil (University of Michigan - Ann Arbor) · Matt Willsey (Stanford University) · Anne Draelos (University of Michigan - Ann Arbor) · Cynthia Chestek (University of Michigan - Ann Arbor)
decoder recalibrationdecoding performanceintracortical brain-machine interfaceskinematic datalatent computationslinear modelslongitudinal analysesneural data decodingneural instabilitiesneural network modelsnonstationarityshift distributionsspiking activitystandardized datasetstemporal data adaptation

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.