EEG-based recognition of activities and states involves the use of prior neuroscience knowledge to generate quantitative EEG features, which may limit BCI performance. Although neural network-based methods can effectively extract features, they often encounter issues such as poor generalization across datasets, high predicting volatility, and low model interpretability. Hence, we propose a novel lightweight multi-dimensional attention network, called LMDA-Net. By incorporating two novel attention modules designed specifically for EEG signals, the channel attention module and the depth attention module, LMDA-Net can effectively integrate features from multiple dimensions, resulting in improved classification performance across various BCI tasks. LMDA-Net was evaluated on four high-impact public datasets, including motor imagery (MI) and P300-Speller paradigms, and was compared with other representative models. The experimental results demonstrate that LMDA-Net outperforms other representative methods in terms of classification accuracy and predicting volatility, achieving the highest accuracy in all datasets within 300 training epochs. Ablation experiments further confirm the effectiveness of the channel attention module and the depth attention module. To facilitate an in-depth understanding of the features extracted by LMDA-Net, we propose class-specific neural network feature interpretability algorithms that are suitable for event-related potentials (ERPs) and event-related desynchronization/synchronization (ERD/ERS). By mapping the output of the specific layer of LMDA-Net to the time or spatial domain through class activation maps, the resulting feature visualizations can provide interpretable analysis and establish connections with EEG time-spatial analysis in neuroscience. In summary, LMDA-Net shows great potential as a general online decoding model for various EEG tasks.

翻译：基于脑电图（EEG）的活动与状态识别通常依赖先验神经科学知识生成定量EEG特征，这可能限制脑机接口（BCI）的性能。尽管基于神经网络的方法能有效提取特征，但常面临跨数据集泛化能力差、预测波动性高及模型可解释性低等问题。为此，我们提出一种新颖的轻量级多维注意力网络——LMDA-Net。通过引入针对EEG信号设计的两个新型注意力模块（通道注意力模块与深度注意力模块），LMDA-Net能有效融合多个维度的特征，从而提升各类BCI任务的分类性能。我们在包含运动想象（MI）和P300-Speller范式在内的四个高影响力公开数据集上对LMDA-Net进行评估，并与代表性模型进行对比。实验结果表明，在分类精度与预测波动性方面，LMDA-Net优于其他代表性方法：在300个训练周期内，它在所有数据集中均达到最高准确率。消融实验进一步验证了通道注意力模块与深度注意力模块的有效性。为深入理解LMDA-Net提取的特征，我们提出了适用于事件相关电位（ERP）及事件相关去同步/同步（ERD/ERS）的类特异性神经网络特征可解释性算法。通过类别激活图将LMDA-Net特定层的输出映射至时域或空域，所得特征可视化结果可提供可解释性分析，并与神经科学中EEG时间-空间分析建立关联。综上所述，LMDA-Net作为多种EEG任务的通用在线解码模型展现出巨大潜力。