  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.
