# Learning Residual Kinematic Corrections for Continuous Neural Decoding via Reinforcement Learning

arXiv: https://arxiv.org/abs/2607.11530 · HTML: https://mlsanity.com/paper/2607.11530/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Deep RL algorithms and representations, one of 15 directions in Deep reinforcement learning methods.
- Builds on 8 of its 47 references that are in the map.
- First posted on arXiv 13 Jul 2026.

## Placed in

- Deep reinforcement learning methods / [Deep RL algorithms and representations](https://mlsanity.com/deeprl/deep-rl-algorithms-and-representations/)

## Builds on (most cited first, 8 of 8)

- [Optuna: A Next-generation Hyperparameter Optimization Framework](https://mlsanity.com/paper/1907.10902/) (2019)
- [Soft Actor-Critic Algorithms and Applications](https://mlsanity.com/paper/1812.05905/) (2018)
- [Residual Reinforcement Learning for Robot Control](https://mlsanity.com/paper/1812.03201/) (2018)
- [Deep Reinforcement Learning for Robotics: A Survey of Real-World Successes](https://mlsanity.com/paper/2408.03539/) (2024)
- [Residual Policy Learning](https://mlsanity.com/paper/1812.06298/) (2018)
- [Residual Reinforcement Learning from Demonstrations](https://mlsanity.com/paper/2106.08050/) (2021)
- [Residual Learning from Demonstration: Adapting DMPs for Contact-rich Manipulation](https://mlsanity.com/paper/2008.07682/) (2020)
- [Embodied Virtual Reality Feedback Reshapes Neural Representations to Support Continuous Three-Dimensional Motor Imagery Decoding](https://mlsanity.com/paper/2605.29677/) (2026)

## Abstract (arXiv)

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. We propose a two-stage decoding framework that applies reinforcement learning (RL) to perform residual kinematic correction on the outputs of a CNN--LSTM decoder (CNN--LSTM--RL). The RL agent is trained offline without direct EEG input and instead operates on predicted kinematic trajectories to optimize movement accuracy relative to target trajectories. Decoding performance was quantified using Pearson correlation coefficients (r) and Root Mean Square Errors (RMSE) along the x, y, and z axes. Compared to CNN--LSTM applied alone, CNN--LSTM--RL improved the mean correlation from 0.5076 to 0.7181 (p = 0.0005) in 2D and from 0.6420 to 0.7780 (p = 0.0059) in VR, with relative gains of 41.5% and 21.2%, respectively. Correspondingly, RMSE was reduced from 0.0890 to 0.0532 (2D, p < 0.0001) and from 0.0714 to 0.0441 (VR, p < 0.0001), representing relative reductions of 40.2% and 38.2%. These findings demonstrate that this scalable framework enhances 3D BCI MI decoding by correcting kinematic errors via offline residual RL without extra neural data, advancing neurorehabilitation, prosthetics, and virtual interaction.