← Back to all news
Hugging Face Trending Papers

Learning Residual Kinematic Corrections for Continuous Neural Decoding via Reinforcement Learning

Read the original on Hugging Face Trending Papers →

Decoding continuous three-dimensional (3D) motor imagery (MI) using non-invasive electroencephalography (EEG)-based brain--computer interfaces (BCIs) remains challenging due to signal variability and residual decoding errors. Deep learning architectures such as convolutional neural network--long short-term memory (CNN--LSTM) models can capture spatial and temporal dynamics for continuous kinematic decoding; however, systematic residual errors persist in predicted trajectories.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at Hugging Face Trending Papers.

Related stories

arXiv AI
Jul 14

Learning Residual Kinematic Corrections for Continuous Neural Decoding via Reinforcement Learning

arXiv:2607. 11530v1 Announce Type: new Abstract: Decoding continuous three-dimensional (3D) motor imagery (MI) using non-invasive electroencephalography (EEG)-based brain--computer interfaces (BCIs) remains challenging due to signal variability and residual decoding errors.

By Jiamian Li, Niall McShane, Attila Korik, Naomi du Bois, Karl McCreadie, Leen Jabban, Benjamin Metcalfe, \"Ozg\"ur \c{S}im\c{s}ek, Damien Coyle
agentsreinforcement-learning
More like this →
arXiv Machine Learning
Jun 9

How Much Capacity Does EEG Denoising Need? Ultra-Compact Networks reveal Benchmark Saturation and Metric-Utility Gap

arXiv:2606. 08594v1 Announce Type: new Abstract: Deep learning EEG denoising architectures have scaled from tens of thousands to tens of millions of parameters, yet no prior study has isolated model capacity as the experimental variable or tested whether reconstruction metrics predict downstream neural-signal utility.

By Jasmeet Singh Bindra, Siddharth Panwar, Shubhajit Roy Chowdhury
llmsefficiencybenchmarks
More like this →
arXiv Machine Learning
Sep 14

FRIST: FMRI Representation Informed Shared-space Training Improves EEG-only Individual-Finger BCI Decoding

The paper introduces FRIST, a two‑stage EEG decoding framework that uses fMRI data to learn spectral projections and class geometry, then refines EEG predictions. In experiments with 12 participants, FRIST improves finger‑level motor decoding accuracy across both movement execution and motor imagery tasks, outperforming EEG‑only baselines and generalizing across different EEG backbones. The method demonstrates that fMRI’s spatial resolution can enhance noninvasive EEG‑based BCI performance.

By Jintao Zhang, Yidan Ding, Joshua Kosnoff, Maxim Karrenbach, Hanwen Wang, Bin He
multimodal
More like this →
arXiv AI
Jul 28

Self-Supervised Consistency Enhanced Disentangled Learning for Neural Decoding Generalization in Brain-Machine Interface

arXiv:2607. 24023v1 Announce Type: new Abstract: Brain-Machine Interfaces (BMIs) provide a direct communication pathway between the brain and external devices, enabling humans to control assistive and robotic technologies, with potential applications in rehabilitation, human motor augmentation, and human-centered robotics.

By Jiyu Wei, Di Hong, Zhanjie Zhang, Dazhong Rong, Qinming He, Yueming Wang
roboticsbenchmarks
More like this →