Author name cluster

Dylan Wallace

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3 papers

2 author rows

NeurIPS Conference 2025 Conference Paper

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 Cubillos
Rio Parsons
Joseph Costello

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.

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NeurIPS Conference 2023 Conference Paper

Balancing memorization and generalization in RNNs for high performance brain-machine Interfaces

Joseph Costello
Hisham Temmar
Luis Cubillos
Matthew Mender
Dylan Wallace
Matt Willsey
Parag Patil
Cynthia Chestek

Brain-machine interfaces (BMIs) can restore motor function to people with paralysis but are currently limited by the accuracy of real-time decoding algorithms. Recurrent neural networks (RNNs) using modern training techniques have shown promise in accurately predicting movements from neural signals but have yet to be rigorously evaluated against other decoding algorithms in a closed-loop setting. Here we compared RNNs to other neural network architectures in real-time, continuous decoding of finger movements using intracortical signals from nonhuman primates. Across one and two finger online tasks, LSTMs (a type of RNN) outperformed convolutional and transformer-based neural networks, averaging 18% higher throughput than the convolution network. On simplified tasks with a reduced movement set, RNN decoders were allowed to memorize movement patterns and matched able-bodied control. Performance gradually dropped as the number of distinct movements increased but did not go below fully continuous decoder performance. Finally, in a two-finger task where one degree-of-freedom had poor input signals, we recovered functional control using RNNs trained to act both like a movement classifier and continuous decoder. Our results suggest that RNNs can enable functional real-time BMI control by learning and generating accurate movement patterns.

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IROS Conference 2020 Conference Paper

Multimodal Teleoperation of Heterogeneous Robots within a Construction Environment

Dylan Wallace
Yu Hang He
Jean Chagas Vaz
Leonardo Georgescu
Paul Y. Oh

Automation in construction continues to be a topic of interest for many in industry and academia. However, the dynamic environments presented in construction sites prove these tasks to be difficult to automate reliably. This paper proposes a novel method of teleoperation for multiple heterogeneous robots within a construction environment. The system is achieved by creating a virtual reality interface that allows an operator to control multiple robots both synchronously and asynchronously. Feedback is provided from an array of RGBD cameras, force sensors, and precise odometry data. The DRC-Hubo and Spot robot platforms are used for implementation and experimentation. Experiments include useful tasks for construction including item manipulation and item delivery of tools and components. Results demonstrate the feasibility of implementing the system in a construction environment, including trajectory comparisons, task learning curves, and successful multi-robot collaboration.

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