Motor imagery electroencephalogram (EEG)-based brain-computer interfaces (BCIs) offer significant advantages for individuals with restricted limb mobility. However, challenges such as low signal-to-noise ratio and limited spatial resolution impede accurate feature extraction from EEG signals, thereby affecting the classification accuracy of different actions. To address these challenges, this study proposes an end-to-end dual-branch network (EEG-DBNet) that decodes the temporal and spectral sequences of EEG signals in parallel through two distinct network branches. Each branch comprises a local convolutional block and a global convolutional block. The local convolutional block transforms the source signal from the temporal-spatial domain to the temporal-spectral domain. By varying the number of filters and convolution kernel sizes, the local convolutional blocks in different branches adjust the length of their respective dimension sequences. Different types of pooling layers are then employed to emphasize the features of various dimension sequences, setting the stage for subsequent global feature extraction. The global convolution block splits and reconstructs the feature of the signal sequence processed by the local convolution block in the same branch and further extracts features through the dilated causal convolutional neural networks. Finally, the outputs from the two branches are concatenated, and signal classification is completed via a fully connected layer. Our proposed method achieves classification accuracies of 85.84% and 91.60% on the BCI Competition 4-2a and BCI Competition 4-2b datasets, respectively, surpassing existing state-of-the-art models. The source code is available at https://github.com/xicheng105/EEG-DBNet.

翻译：基于运动想象脑电图（EEG）的脑机接口（BCI）为肢体活动受限的个体提供了显著优势。然而，低信噪比和有限的空间分辨率等挑战阻碍了从EEG信号中准确提取特征，从而影响了不同动作的分类精度。为解决这些挑战，本研究提出了一种端到端的双分支网络（EEG-DBNet），通过两个独立的网络分支并行解码EEG信号的时序与频谱序列。每个分支包含一个局部卷积块和一个全局卷积块。局部卷积块将源信号从时空域转换到时频谱域。通过改变滤波器数量和卷积核大小，不同分支中的局部卷积块调整了各自维度序列的长度。随后采用不同类型的池化层来强调各种维度序列的特征，为后续的全局特征提取奠定基础。全局卷积块对同一分支中经局部卷积块处理后的信号序列特征进行拆分与重构，并通过扩张因果卷积神经网络进一步提取特征。最后，将两个分支的输出进行拼接，并通过全连接层完成信号分类。我们提出的方法在BCI Competition 4-2a和BCI Competition 4-2b数据集上分别达到了85.84%和91.60%的分类准确率，超越了现有的最先进模型。源代码可在https://github.com/xicheng105/EEG-DBNet获取。