Motor imagery electroencephalograph (MI-EEG) decoding plays a crucial role in developing motor imagery brain-computer interfaces (MI-BCIs). However, decoding intentions from MI remains challenging due to the inherent complexity of EEG signals relative to the small-sample size. In this paper, we propose an Efficient Dual Prototype Network (EDPNet) to enable accurate and fast MI decoding. EDPNet employs a lightweight adaptive spatial-spectral fusion module, which promotes more efficient information fusion between multiple EEG electrodes. Subsequently, a parameter-free multi-scale variance pooling module extracts more comprehensive temporal features. Furthermore, we introduce dual prototypical learning to optimize the feature space distribution and training process, thereby improving the model's generalization ability on small-sample MI datasets. Our experimental results show that the EDPNet outperforms state-of-the-art models with superior classification accuracy and kappa values (84.11% and 0.7881 for dataset BCI competition IV 2a, 86.65% and 0.7330 for dataset BCI competition IV 2b). Additionally, we use the BCI competition III IVa dataset with fewer training data to further validate the generalization ability of the proposed EDPNet. We also achieve superior performance with 82.03% classification accuracy. Benefiting from the lightweight parameters and superior decoding accuracy, our EDPNet shows great potential for MI-BCI applications. The code is publicly available at https://github.com/hancan16/EDPNet.

翻译：运动想象脑电信号解码在开发运动想象脑机接口中起着至关重要的作用。然而，由于脑电信号固有的复杂性相对于小样本规模，从运动想象信号中解码意图仍然具有挑战性。本文提出了一种高效双原型网络，以实现准确且快速的运动想象解码。EDPNet采用轻量级自适应空谱融合模块，以促进多个脑电电极之间更高效的信息融合。随后，一个无参数的多尺度方差池化模块提取更全面的时序特征。此外，我们引入了双原型学习来优化特征空间分布和训练过程，从而提升模型在小样本运动想象数据集上的泛化能力。我们的实验结果表明，EDPNet在分类准确率和kappa值上均优于最先进的模型（在BCI竞赛IV 2a数据集上分别为84.11%和0.7881，在BCI竞赛IV 2b数据集上分别为86.65%和0.7330）。此外，我们使用训练数据更少的BCI竞赛III IVa数据集进一步验证了所提EDPNet的泛化能力，并取得了82.03%分类准确率的优异性能。得益于轻量化的参数量和卓越的解码准确率，我们的EDPNet在运动想象脑机接口应用中展现出巨大潜力。代码公开于https://github.com/hancan16/EDPNet。