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A calibration procedure is required in motor imagery-based brain-computerinterface (MI-BCI) to tune the system for new users. This procedure istime-consuming and prevents na\"ive users from using the system immediately.Developing a subject-independent MI-BCI system to reduce the calibration phaseis still challenging due to the subject-dependent characteristics of the MIsignals. Many algorithms based on machine learning and deep learning have beendeveloped to extract high-level features from the MI signals to improve thesubject-to-subject generalization of a BCI system. However, these methods arebased on supervised learning and extract features useful for discriminatingvarious MI signals. Hence, these approaches cannot find the common underlyingpatterns in the MI signals and their generalization level is limited. Thispaper proposes a subject-independent MI-BCI based on a supervised autoencoder(SAE) to circumvent the calibration phase. The suggested framework is validatedon dataset 2a from BCI competition IV. The simulation results show that ourSISAE model outperforms the conventional and widely used BCI algorithms, commonspatial and filter bank common spatial patterns, in terms of the mean Kappavalue, in eight out of nine subjects.

A Subject-Independent Brain-Computer Interface Framework Based on Supervised Autoencoder