  Brain-computer interface (BCI) is challenging to use in practice due to the
inter/intra-subject variability of electroencephalography (EEG). The BCI
system, in general, necessitates a calibration technique to obtain
subject/session-specific data in order to tune the model each time the system
is utilized. This issue is acknowledged as a key hindrance to BCI, and a new
strategy based on domain generalization has recently evolved to address it. In
light of this, we've concentrated on developing an EEG classification framework
that can be applied directly to data from unknown domains (i.e. subjects),
using only data acquired from separate subjects previously. For this purpose,
in this paper, we proposed a framework that employs the open-set recognition
technique as an auxiliary task to learn subject-specific style features from
the source dataset while helping the shared feature extractor with mapping the
features of the unseen target dataset as a new unseen domain. Our aim is to
impose cross-instance style in-variance in the same domain and reduce the open
space risk on the potential unseen subject in order to improve the
generalization ability of the shared feature extractor. Our experiments showed
that using the domain information as an auxiliary network increases the
generalization performance.
