  In the quest for efficient neural network models for neural data
interpretation and user intent classification in brain-computer interfaces
(BCIs), learning meaningful sparse representations of the underlying neural
subspaces is crucial. The present study introduces a sparse multitask learning
framework for motor imagery (MI) and motor execution (ME) tasks, inspired by
the natural partitioning of associated neural subspaces observed in the human
brain. Given a dual-task CNN model for MI-ME classification, we apply a
saliency-based sparsification approach to prune superfluous connections and
reinforce those that show high importance in both tasks. Through our approach,
we seek to elucidate the distinct and common neural ensembles associated with
each task, employing principled sparsification techniques to eliminate
redundant connections and boost the fidelity of neural signal decoding. Our
results indicate that this tailored sparsity can mitigate the overfitting
problem and improve the test performance with small amount of data, suggesting
a viable path forward for computationally efficient and robust BCI systems.
