Joseph T Costello — accepted AI-conference papers

2025

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 +12

NeurIPS 2025poster

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, a…

Cited by 0 Source ↗code ↗Scholar ↗

2024

Exploring the trade-off between deep-learning and explainable models for brain-machine interfaces

Luis Hernan Cubillos, Guy Revach, Matthew Mender, Joseph T Costello, Hisham Temmar, Aren Hite +10

NeurIPS 2024poster

People with brain or spinal cord-related paralysis often need to rely on others for basic tasks, limiting their independence. A potential solution is brain-machine interfaces (BMIs), which could allow them to voluntarily control external devices (e.g., robotic arm) by decoding brain activity to move…

Cited by 9 Source ↗PDF ↗Scholar ↗

2023

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

Joseph T Costello, Hisham Temmar, Luis H Cubillos, Matthew J Mender, Dylan M Wallace, Matthew S Willsey +2

NeurIPS 2023spotlight

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…

Cited by 15 Source ↗PDF ↗Scholar ↗