  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.
