  A brain-computer interface (BCI) can't be effectively used since
electroencephalography (EEG) varies between and within subjects. BCI systems
require calibration steps to adjust the model to subject-specific data. It is
widely acknowledged that this is a major obstacle to the development of BCIs.
To address this issue, previous studies have trained a generalized model by
removing the subjects' information. In contrast, in this work, we introduce a
style information encoder as an auxiliary task that classifies various source
domains and recognizes open-set domains. Open-set recognition method was used
as an auxiliary task to learn subject-related style information from the source
subjects, while at the same time helping the shared feature extractor map
features in an unseen target. This paper compares various OSR methods within an
open-set subject recognition (OSSR) framework. As a result of our experiments,
we found that the OSSR auxiliary network that encodes domain information
improves generalization performance.
