Motor imagery (MI) EEG signal classification is a critical issue for brain–computer interface (BCI) systems. In traditional MI EEG machine learning algorithms, feature extraction and classification often have different objective functions, thus resulting in information loss. To solve this problem, a novel spatial-frequency-temporal (SFT) 3D CNN model is proposed. Specifically, the energies of EEG signals located in multiple local SFT ranges are extracted to obtain a novel 3D MI EEG feature representation, and a novel 3D CNN model is designed to simultaneously learn the complex MI EEG features in the entire SFT domains and carry out classification. An extensive experimental study is implemented on two public EEG datasets to evaluate the effectiveness of our method. For BCI Competition III Dataset IVa, the average accuracy rate of five subjects obtained by the proposed method reaches 86.6% and yields 4.1% improvement over the state-of-the-art filter band common spatial pattern (FBCSP) method. For BCI Competition III dataset IIIa, by achieving an average accuracy rate of 91.85%, the proposed method outperforms the state-of-the-art dictionary pair learning (DPL) method by 4.44%.

运动图像（MI）脑电信号分类是脑机接口（BCI）系统的关键问题。在传统的MI EEG机器学习算法中，特征提取和分类通常具有不同的目标函数，从而导致信息丢失。为了解决这个问题，提出了一种新型的时空时空（SFT）3D CNN模型。具体地，提取位于多个局部SFT范围内的EEG信号的能量以获得新颖的3D MI EEG特征表示，并且设计新颖的3D CNN模型以同时学习整个SFT域中的复杂MI EEG特征并进行分类。 。在两个公共EEG数据集上进行了广泛的实验研究，以评估我们方法的有效性。对于BCI竞赛III数据集IVa，通过该方法获得的五个对象的平均准确率达到了86.6％，与最新的滤波器带公共空间模式（FBCSP）方法相比，提高了4.1％。对于BCI竞赛III数据集IIIa，通过达到91.85％的平均准确率，该方法的表现优于最新的字典对学习（DPL）方法4.44％。