  The cross-subject application of EEG-based brain-computer interface (BCI) has
always been limited by large individual difference and complex characteristics
that are difficult to perceive. Therefore, it takes a long time to collect the
training data of each user for calibration. Even transfer learning method
pre-training with amounts of subject-independent data cannot decode different
EEG signal categories without enough subject-specific data. Hence, we proposed
a cross-subject EEG classification framework with a generative adversarial
networks (GANs) based method named common spatial GAN (CS-GAN), which used
adversarial training between a generator and a discriminator to obtain
high-quality data for augmentation. A particular module in the discriminator
was employed to maintain the spatial features of the EEG signals and increase
the difference between different categories, with two losses for further
enhancement. Through adaptive training with sufficient augmentation data, our
cross-subject classification accuracy yielded a significant improvement of
15.85% than leave-one subject-out (LOO) test and 8.57% than just adapting 100
original samples on the dataset 2a of BCI competition IV. Moreover, We designed
a convolutional neural networks (CNNs) based classification method as a
benchmark with a similar spatial enhancement idea, which achieved remarkable
results to classify motor imagery EEG data. In summary, our framework provides
a promising way to deal with the cross-subject problem and promote the
practical application of BCI.
