MIN2Net: End-to-End Multi-Task Learning for Subject-Independent Motor Imagery EEG Classification. (arXiv:2102.03814v4 [eess.SP] UPDATED)

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 …

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