[Multi-scale feature extraction and classification of motor imagery electroencephalography based on time series data enhancement].

The brain-computer interface (BCI) based on motor imagery electroencephalography (MI-EEG) enables direct information interaction between the human brain and external devices. In this paper, a multi-scale EEG feature extraction convolutional neural network model based on time series data enhancement is proposed for decoding MI-EEG signals. First, an EEG signals augmentation method was proposed that could increase the information content of training samples without changing the length of the time series, while retaining its original features completely. Then, multiple holistic and detailed features of the EEG data were adaptively extracted by multi-scale convolution module, and the features were fused and filtered by parallel residual module and channel attention. Finally, classification results were output by a fully connected network. The application experimental results on the BCI Competition IV 2a and 2b datasets showed that the proposed model achieved an average classification accuracy of 91.87% and 87.85% for the motor imagery task, respectively, which had high accuracy and strong robustness compared with existing baseline models. The proposed model does not require complex signals pre-processing operations and has the advantage of multi-scale feature extraction, which has high practical application value.基于运动想象脑电（MI-EEG）的脑机接口（BCI）可以实现人脑与外部设备的直接信息交互。本文提出了一种基于时间序列数据增强的脑电多尺度特征提取卷积神经网络模型，用于MI-EEG信号解码。首先，提出了一种脑电信号数据增强方法，能够在不改变时间序列长度的情况下，提高训练样本的信息含量，同时完整保留其初始特征。然后，通过多尺度卷积块自适应地提取脑电数据的多种整体与细节特征，再经并行残差块和通道注意力对特征进行融合筛选。最后，由全连接网络输出分类结果。在BCI Competition IV 2a和2b数据集上的应用实验结果表明，本模型对运动想象任务的平均分类正确率分别达到了91.87%和87.85%，对比现有的基准模型，该方法具有较高的正确率和较强的鲁棒性。该模型无需复杂的信号预处理操作，具有多尺度特征提取的优势，具有较高的实际应用价值。.