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.

翻译：运动成像(MI)基于大脑-计算机界面(BCIs)的进步使得能够通过解码神经生理现象来控制多种应用,这些现象通常通过使用非侵入技术进行电子脑物理学记录。尽管在以MI为基础的BCI方面取得了巨大进步,但EEG节奏是针对某个主题的,随着时间的推移发生了各种变化。这些问题表明提高分类性能的重大挑战,特别是以独立主体的方式。为了克服这些挑战,我们提议MIN2Net,这是应对这项任务的新型端到端多任务学习。我们将深层次的计量学习纳入多塔斯克自动编码器,以学习EEEG的紧凑和歧视性潜势,同时进行分类。这种方法降低了预处理的复杂性,使EEG分类的性能得到显著改善。以依赖主体的方式实验结果显示,MIN2Net超越了最新技术,实现了6.72%的F-1-核心改进,在SMR-BI和OpenBMI数据库中实现了2.23%的深度学习,并同时进行分类。我们展示了在不使用歧视性数据库的用户中改进了新的BII数据格式和BI数据库应用的可能性。我们分别发展了该数据库的新的数据库的模型的模型的模型和BID-BS-CRED-Credistris