Learning Residual Kinematic Corrections for Continuous Neural Decoding

Directly improving continuous three-dimensional kinematic decoding from non-invasive EEG remains challenging because of its low signal-to-noise ratio, variability, and non-stationarity. 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 decoded trajectories. We propose a two-stage decoding framework that applies a residual error learning model to perform kinematic correction on the outputs of a CNN–LSTM decoder. The residual model is trained offline without direct EEG input and instead operates on predicted kinematic trajectories to optimize movement accuracy relative to target trajectories. This design enables targeted correction of systematic decoder errors while preserving the primary neural decoding pipeline. The proposed framework was evaluated offline using data from ten participants across ten sessions of online continuous 3D motor imagery, with feedback provided in both 2D and immersive virtual reality (VR) environments. Decoding performance was quantified using Pearson correlation coefficients ($r$) and root mean square error (RMSE) along the $x$, $y$, and $z$ axes. Compared with the CNN–LSTM applied alone, the CNN–LSTM with residual error correction improved the mean correlation from 0.5076 to 0.7182 ($p<0.05$) in 2D and from 0.6420 to 0.7783 ($p<0.05$) in VR, corresponding to relative improvements of 41.5% and 21.2%, respectively. Correspondingly, RMSE was reduced from 0.0564 to 0.0380 in 2D ($p<0.05$) and from 0.0528 to 0.0352 in VR ($p<0.05$), representing relative reductions of 32.6% and 33.3%, respectively. These findings demonstrate that this scalable framework enhances continuous 3D MI decoding in BCIs by correcting kinematic errors using an offline residual model without requiring additional neural data, thereby advancing applications in neurorehabilitation, prosthetics, and virtual interaction.