MIN2Net: End-to-End Multi-Task Learning for Subject-Independent Motor Imagery EEG Classification
This work is significant for new users of MI-based BCI applications, as it aims to reduce or eliminate the need for calibration by improving subject-independent classification performance, which is an incremental step towards more practical BCI systems.
This paper addresses the challenge of subject-independent motor imagery (MI) EEG classification by proposing MIN2Net, an end-to-end multi-task learning framework. It integrates deep metric learning into a multi-task autoencoder to learn compact and discriminative latent representations, achieving F1-score improvements of 6.72% on SMR-BCI and 2.23% on OpenBMI datasets compared to state-of-the-art techniques.
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.