MIN2Net: End-to-End Multi-Task Learning for Subject-Independent Motor Imagery EEG Classification

\textit{Objective:} 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 significant 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. \textit{Methods:} 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. \textit{Results:} 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. \textit{Conclusion:} We demonstrate that MIN2Net improves discriminative information in the latent representation. \textit{Significance:} This study indicates the possibility and practicality of using this model to develop MI-based BCI applications for new users without calibration.