Motor imagery electroencephalogram (EEG)-based brain-computer interfaces (BCIs) aid individuals with restricted limb mobility. However, challenges like low signal-to-noise ratio and limited spatial resolution hinder accurate feature extraction from EEG signals, impacting classification. To tackle these issues, we propose an end-to-end dual-branch neural network (EEG-DBNet). This network decodes temporal and spectral sequences separately using distinct branches. Each branch has local and global convolution blocks for extracting local and global features. The temporal branch employs three convolutional layers with smaller kernels, fewer channels, and average pooling, while the spectral branch uses larger kernels, more channels, and max pooling. Global convolution blocks then extract comprehensive features. Outputs from both branches are concatenated and fed to fully connected layers for classification. Ablation experiments demonstrate that our architecture, with specialized convolutional parameters for temporal and spectral sequences, significantly improves classification accuracy compared to single-branch structures. The complementary relationship between local and global convolutional blocks compensates for traditional CNNs' limitations in global feature extraction. Our method achieves accuracies of 85.84% and 91.42% on BCI Competition 4-2a and 4-2b datasets, respectively, surpassing existing state-of-the-art models. Source code is available at https://github.com/xicheng105/EEG-DBNet.

翻译：基于运动想象脑电图的脑机接口有助于肢体活动受限的个体。然而，低信噪比和有限的空间分辨率等挑战阻碍了从脑电信号中准确提取特征，从而影响分类性能。为解决这些问题，我们提出了一种端到端的双分支神经网络。该网络使用不同的分支分别解码时序和频谱序列。每个分支均包含局部和全局卷积块，用于提取局部和全局特征。时序分支采用三个卷积层，其卷积核较小、通道数较少并配合平均池化；而频谱分支则使用较大的卷积核、更多的通道数并配合最大池化。随后，全局卷积块提取综合特征。两个分支的输出被拼接后输入全连接层进行分类。消融实验表明，与单分支结构相比，我们针对时序和频谱序列专门设计卷积参数的架构显著提高了分类准确率。局部与全局卷积块之间的互补关系弥补了传统卷积神经网络在全局特征提取方面的局限性。我们的方法在BCI Competition 4-2a和4-2b数据集上分别达到了85.84%和91.42%的准确率，超越了现有最先进的模型。源代码可在https://github.com/xicheng105/EEG-DBNet获取。