Motor imagery classification based on electroencephalography (EEG) signals is one of the most important brain-computer interface applications, although it needs further improvement. Several methods have attempted to obtain useful information from EEG signals by using recent deep learning techniques such as transformers. To improve the classification accuracy, this study proposes a novel EEG-based motor imagery classification method with three key features: generation of a topological map represented as a two-dimensional image from EEG signals with coordinate transformation based on t-SNE, use of the InternImage to extract spatial features, and use of spatiotemporal pooling inspired by PoolFormer to exploit spatiotemporal information concealed in a sequence of EEG images. Experimental results using the PhysioNet EEG Motor Movement/Imagery dataset showed that the proposed method achieved the best classification accuracy of 88.57%, 80.65%, and 70.17% on two-, three-, and four-class motor imagery tasks in cross-individual validation.

翻译：基于脑电图信号的运动想象分类是最重要的脑-机接口应用之一，但仍需进一步改进。多种方法尝试通过Transformer等最新深度学习技术从脑电信号中获取有效信息。为提高分类精度，本研究提出了一种新颖的基于脑电图的运动想象分类方法，具有三个关键特征：通过基于t-SNE的坐标变换从脑电信号生成表示为二维图像的拓扑图、采用InternImage提取空间特征、以及利用受PoolFormer启发的时空池化挖掘脑电图图像序列中隐藏的时空信息。使用PhysioNet脑电运动/想象数据集进行的实验表明，在跨个体验证中，所提方法在二类、三类和四类运动想象任务上分别达到了88.57%、80.65%和70.17%的最佳分类精度。