Matthew Mender — 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 ↗

2024

Few-shot Algorithms for Consistent Neural Decoding (FALCON) Benchmark

Brianna M. Karpowicz, Joel Ye, Chaofei Fan, Pablo Tostado-Marcos, Fabio Rizzoglio, Clayton B Washington +16

NeurIPS 2024poster

Intracortical brain-computer interfaces (iBCIs) can restore movement and communication abilities to individuals with paralysis by decoding their intended behavior from neural activity recorded with an implanted device. While this activity yields high-performance decoding over short timescales, neura…

Cited by 1 Source ↗PDF ↗code ↗Scholar ↗