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 overcome this issue, we proposed MIN2Net, a novel end-to-end neural network architecture and loss function for training multi- task AE in the MI ...

Python API and the novel algorithm for motor imagery EEG recognition named MIN2Net. The API benefits BCI researchers ranging from beginners to experts.

This study indicates the possibility and practicality of using MIN2Net, a novel end-to-end multi-task learning to develop MI-based BCI applications for new ...

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

Motor imagery (MI) classification based on Electroencephalography (EEG) signal analysis has received a lot of attention for the purpose of movement intent ...

We integrate deep metric learning into a multi-task autoencoder to learn a compact and discriminative latent representation from EEG and perform classification ...

Feb 7, 2021 · We integrate deep metric learning into a multi-task autoencoder to learn a compact and discriminative latent representation from EEG and perform ...

MIN2Net [6] utilized deep metric learning and autoencoder for subjectindependent motor imagery EEG signal classification, outperforming state-ofthe-art ...

MIN2Net: End-to-End Multi-Task Learning for Subject-Independent Motor Imagery EEG Classification. Visit Snyk Advisor to see a full health score report for ...