Classification of electroencephalogram-based motor imagery (MI-EEG) tasks raises a big challenge in the design and development of brain–computer interfaces (BCIs). In view of the characteristics of nonstationarity, time-variability, and individual diversity of EEG signals, a deep learning framework termed SSD-SE-convolutional neural network (CNN) is proposed for MI-EEG classification. The framework consists of three parts: 1) the sparse spectrotemporal decomposition (SSD) algorithm is proposed for feature extraction, overcoming the drawbacks of conventional time–frequency analysis methods and enhancing the robustness to noise; 2) a CNN is constructed to fully exploit the time–frequency features, thus outperforming traditional classification methods both in terms of accuracy and kappa value; and 3) the squeeze-and-excitation (SE) blocks are adopted to adaptively recalibrate channelwise feature responses, which further improves the overall performance and offers a compelling classification solution for MI-EEG applications. Experimental results on two datasets reveal that the proposed framework outperforms state-of-the-art methods in terms of both classification quality and robustness. The advantages of SSD-SE-CNN include high accuracy, high efficiency, and robustness to cross-trial and cross-session variations, making it an ideal candidate for long-term MI-EEG applications. Note to Practitioners —Motor imagery-based brain–computer interfaces (MI-BCIs) are widely used to allow a user to control a device using only his or her neural activity. This article proposed a new framework to classify two-class MI tasks based on electroencephalography (EEG) signals. In this framework, a new sparse spectrotemporal decomposition method is used to extract time–frequency features from EEG signals. A convolutional neural network with squeeze-and-excitation blocks is then constructed to classify the MI tasks. We show the superiority of our method on two datasets and prove its feasibility for long-term MI-BCI applications.

中文翻译：

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稀疏时空分解和深度学习的脑电图运动图像分类

基于脑电图的运动图像（MI-EEG）任务的分类在脑机接口（BCI）的设计和开发中提出了很大的挑战。鉴于脑电信号的非平稳性，时变性和个体多样性的特点，提出了一种称为SSD-SE-卷积神经网络（CNN）的深度学习框架，用于MI-EEG分类。该框架包括三个部分：1）提出了一种稀疏的时空分解（SSD）算法进行特征提取，克服了传统的时频分析方法的弊端，增强了噪声的鲁棒性。2）构建CNN以充分利用时频特征，因此在准确性和kappa值方面均优于传统分类方法；3）采用挤压和激励（SE）模块来自适应地重新校准通道特征响应，这进一步提高了整体性能，并为MI-EEG应用提供了引人注目的分类解决方案。在两个数据集上的实验结果表明，在分类质量和鲁棒性方面，所提出的框架均优于最新方法。SSD-SE-CNN的优势包括高精度，高效率以及对交叉尝试和跨会话变化的鲁棒性，使其成为长期MI-EEG应用的理想选择。在两个数据集上的实验结果表明，在分类质量和鲁棒性方面，所提出的框架均优于最新方法。SSD-SE-CNN的优点包括高精度，高效率以及对交叉尝试和跨会话变化的鲁棒性，使其成为长期MI-EEG应用的理想选择。在两个数据集上的实验结果表明，在分类质量和鲁棒性方面，所提出的框架均优于最新方法。SSD-SE-CNN的优点包括高精度，高效率以及对交叉尝试和跨会话变化的鲁棒性，使其成为长期MI-EEG应用的理想选择。执业者须知 基于运动图像的脑机接口（MI-BCI）被广泛用于允许用户仅使用其神经活动来控制设备。本文提出了一种基于脑电图（EEG）信号对两类MI任务进行分类的新框架。在此框架中，使用了一种新的稀疏时空分解方法来从EEG信号中提取时频特征。然后构造具有挤压和激励块的卷积神经网络以对MI任务进行分类。我们在两个数据集上展示了我们方法的优越性，并证明了其在长期MI-BCI应用中的可行性。