  Recently, advanced technologies have unlimited potential in solving various
problems with a large amount of data. However, these technologies have yet to
show competitive performance in brain-computer interfaces (BCIs) which deal
with brain signals. Basically, brain signals are difficult to collect in large
quantities, in particular, the amount of information would be sparse in
spontaneous BCIs. In addition, we conjecture that high spatial and temporal
similarities between tasks increase the prediction difficulty. We define this
problem as sparse condition. To solve this, a factorization approach is
introduced to allow the model to obtain distinct representations from latent
space. To this end, we propose two feature extractors: A class-common module is
trained through adversarial learning acting as a generator; Class-specific
module utilizes loss function generated from classification so that features
are extracted with traditional methods. To minimize the latent space shared by
the class-common and class-specific features, the model is trained under
orthogonal constraint. As a result, EEG signals are factorized into two
separate latent spaces. Evaluations were conducted on a single-arm motor
imagery dataset. From the results, we demonstrated that factorizing the EEG
signal allows the model to extract rich and decisive features under sparse
condition.
