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.

为了提高大脑计算机接口（BCI）中神经数据解释和用户意图分类的效率，本研究引入了稀疏多任务学习框架，用于运动想象和运动执行任务，通过采用基于显著性的稀疏化方法来剪枝多余的连接并增强两个任务中具有高重要性的连接，以揭示与每个任务相关的不同和共同的神经组合，并使用原则性稀疏化技术消除多余连接并提高神经信号解码的准确性，结果表明这种定制稀疏性可以减轻过拟合问题，改善少量数据的测试性能，为计算效率高且稳健的 BCI 系统提供了可行的发展方向。