  Advances in the motor imagery (MI)-based brain-computer interfaces (BCIs)
allow control of several applications by decoding neurophysiological phenomena,
which are usually recorded by electroencephalography (EEG) using a non-invasive
technique. Despite great advances in MI-based BCI, EEG rhythms are specific to
a subject and various changes over time. These issues point to significant
challenges to enhance the classification performance, especially in a
subject-independent manner. To overcome these challenges, we propose MIN2Net, a
novel end-to-end multi-task learning to tackle this task. We integrate deep
metric learning into a multi-task autoencoder to learn a compact and
discriminative latent representation from EEG and perform classification
simultaneously. This approach reduces the complexity in pre-processing, results
in significant performance improvement on EEG classification. Experimental
results in a subject-independent manner show that MIN2Net outperforms the
state-of-the-art techniques, achieving an F1-score improvement of 6.72%, and
2.23% on the SMR-BCI, and OpenBMI datasets, respectively. We demonstrate that
MIN2Net improves discriminative information in the latent representation. This
study indicates the possibility and practicality of using this model to develop
MI-based BCI applications for new users without the need for calibration.
