  Brain-computer interface uses brain signals to communicate with external
devices without actual control. Many studies have been conducted to classify
motor imagery based on machine learning. However, classifying imagery data with
sparse spatial characteristics, such as single-arm motor imagery, remains a
challenge. In this paper, we proposed a method to factorize EEG signals into
two groups to classify motor imagery even if spatial features are sparse. Based
on adversarial learning, we focused on extracting common features of EEG
signals which are robust to noise and extracting only signal features. In
addition, class-specific features were extracted which are specialized for
class classification. Finally, the proposed method classifies the classes by
representing the features of the two groups as one embedding space. Through
experiments, we confirmed the feasibility that extracting features into two
groups is advantageous for datasets that contain sparse spatial features.
