  A calibration procedure is required in motor imagery-based brain-computer
interface (MI-BCI) to tune the system for new users. This procedure is
time-consuming and prevents na\"ive users from using the system immediately.
Developing a subject-independent MI-BCI system to reduce the calibration phase
is still challenging due to the subject-dependent characteristics of the MI
signals. Many algorithms based on machine learning and deep learning have been
developed to extract high-level features from the MI signals to improve the
subject-to-subject generalization of a BCI system. However, these methods are
based on supervised learning and extract features useful for discriminating
various MI signals. Hence, these approaches cannot find the common underlying
patterns in the MI signals and their generalization level is limited. This
paper proposes a subject-independent MI-BCI based on a supervised autoencoder
(SAE) to circumvent the calibration phase. The suggested framework is validated
on dataset 2a from BCI competition IV. The simulation results show that our
SISAE model outperforms the conventional and widely used BCI algorithms, common
spatial and filter bank common spatial patterns, in terms of the mean Kappa
value, in eight out of nine subjects.
