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

TL;DR

MIN2Net is an end-to-end multi-task learning framework that enhances subject-independent motor imagery EEG classification by integrating deep metric learning with a multi-task autoencoder, leading to improved performance and practicality for BCI applications.

Contribution

This paper introduces MIN2Net, a novel deep learning model combining multi-task autoencoders and metric learning for improved EEG classification without calibration.

Findings

MIN2Net outperforms state-of-the-art methods with 6.72% F1-score improvement on SMR-BCI.

MIN2Net achieves 2.23% F1-score improvement on OpenBMI dataset.

The model enhances discriminative features in the latent space for better classification.

Abstract

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.