Learning Residual Kinematic Corrections for Continuous Neural Decoding via Reinforcement Learning

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.